• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.677-682
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• 2023

A large number of motors and sensors are used to drive a humanoid robot. In order to solve the wiring problem that occurs when connecting multiple actuators, a controller based on a communication network has been used, and CAN, which is advantageous in terms of cost and a highly reliable communication protocol, was mainly used. In terms of the structure of the controller, a torque control type structure that is easy to implement an advanced algorithm into the upper controller is preferred. In this case, the low communication bandwidth of CAN becomes a problem, and in order to obtain sufficient communication bandwidth, a communication network is configured by separating into a plurality of CAN networks. In this study, a stability analysis on transmission time delay is performed for a multi-motor control system in which high-speed FlexRay and low-speed CAN communication networks are hierarchically connected in order to obtain a high communication bandwidth, and sensor information and driving signals are delivered within the allowed transmission time. The proposed hierarchical network-based control system is expected to improve control performance because it can implement multiple motor control systems with a single network.

• Journal of Technology Innovation
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• v.22 no.3
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• pp.65-100
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• 2014

We tried to find out the technological innovation properties of the National Research and Development Projects (NRDPs) through scientometric analysis of patent data generated by the NRDPs. The distribution of technology groups in patent data shows that NRDPs are highly focused on the Information Technology(IT) group. The Relative Cites Index(RCI) analysis implies that the Biotechnology(BT) group will emerge in the future. The Relative Family Indices(RFIs) are high among the high technological impact groups such as BT and IT. We defined new measures related with technological convergence and collaborativenessopenness relations among R&D agents. Using network analysis techniques, we analysed the absorption-derivation relations among technology groups and the opennesscollaborativeness relations among R&D agents. Recently, both the share of hetero-plural technology groups and the collaboration among different R&D agents are increasing.

• Proceedings of the Korean Society for Quality Management Conference
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• 1998.11a
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• pp.696-704
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• 1998

Artificial neural networks (ANN) have successfully applied into various areas. But, How to effectively established network is the one of the critical problem. This study will focus on this problem and try to extensively study. Firstly, four different learning algorithms ANNs were constructed. The learning algorithms include backpropagation, simulated annealing, genetic algorithm, and tabu search. The experimental results of the above four different learning algorithms were tested by statistical analysis. The training RMS, training time, and testing RMS were used as the comparison criteria.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2010.04a
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• pp.177-180
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• 2010

In cadastral field GPS mainly applies to fundamental survey, while there are numerous research about cadastral detail survey using GPS application in order to increase surveying efficiency as survey technology improve. The purpose of this experiment is to analyze the accuracy of position and estimate the efficiency of GPS/GLONASS combination surveying with control points. As the result of this experiment, Network RTK-GPS/GLONASS combination survey is superior to Newtork RTK-GPS with respect to position accuracy and work efficiency.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.794_795
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• 2009

This paper presents an analysis method for a permanent magnet excited Transverse Flux Linear Motor (TFLM) with spiral core in a mover. The spiral core is used as mover core in order to make 3-dimensional magnetic flux path at the TFLM which has 3-dimensional magnetic flux flow. Magnetic field is analyzed by three-dimensional Equivalent Magnetic Circuit Network (EMCN) method. And an imaginary part, 'flux barrier,' is introduced to consider the spiral core characteristic. The computed thrust forces is compared to the measured results to show the effect of presented analysis method.

• Journal of the Korea Institute of Military Science and Technology
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• v.10 no.2
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• pp.107-114
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• 2007

This paper describes the Petri Net(PN) model for reliability analysis of fault-tolerant dual Ethernet which Is applied in Naval Combat System. The network for Naval Combat System performs failure detection and auto path recovery by handling redundant path in case of temporary link failure. After studying the behavior of this kind of network, the reliability analysis model is proposed using stochastic Petri Net and continuous-time Markov chains. Finally, the numerical result is analyzed according to changing the failure rate and the recover rate of link.

• The Journal of Society for e-Business Studies
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• v.14 no.3
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• pp.169-181
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• 2009

As the situations that important evidences or clues are found in digital information devices increase, digital forensic technology is widely applied. In this paper, forensic based audit system is implemented by associating forensic analysis system with agent system which monitors and collects data for analysis in storage devices over distributed network nodes. Forensic audit system implemented in this paper can prevent, audit and trace the computer related crimes in IT infrastructure by real time monitoring and evidence seizure.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.11a
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• pp.417-426
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• 2000

This study suggests integrated neural network models for Interest rate forecasting using change-point detection, classifiers, and classification functions based on structural change. The proposed model is composed of three phases with tee-staged learning. The first phase is to detect successive and appropriate structural changes in interest rare dataset. The second phase is to forecast change-point group with classifiers (discriminant analysis, logistic regression, and backpropagation neural networks) and their. combined classification functions. The fecal phase is to forecast the interest rate with backpropagation neural networks. We propose some classification functions to overcome the problems of two-staged learning that cannot measure the performance of the first learning. Subsequently, we compare the structured models with a neural network model alone and, in addition, determine which of classifiers and classification functions can perform better. This article then examines the predictability of the proposed classification functions for interest rate forecasting using structural change.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.31-36
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• 2021

RNA sequencing (RNA-Seq) is a technology that facilitates transcriptome analysis using next-generation sequencing (NSG) tools. Information on the quantity and sequences of RNA is vital to relate our genomes to functional protein expression. RNA-Seq data are characterized as being high-dimensional in that the number of variables (i.e., transcripts) far exceeds the number of observations (e.g., experiments). Given the wide range of dimensionality reduction techniques, it is not clear which is best for RNA-Seq data analysis. In this paper, we study the effect of three dimensionality reduction techniques to improve the classification of the RNA-Seq dataset. In particular, we use PCA, SVD, and SOM to obtain a reduced feature space. We built nine classification models for a cancer dataset and compared their performance. Our experimental results indicate that better classification performance is obtained with PCA and SOM. Overall, the combinations PCA+KNN, SOM+RF, and SOM+KNN produce preferred results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.11
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• pp.5301-5323
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• 2017

Small cell deployment offers a low-cost solution for the boosted traffic demand in heterogeneous cellular networks (HCNs). Besides improved spatial spectrum efficiency and energy efficiency, future HCNs are also featured with the trend of network architecture convergence and feasibility for flexible mobile applications. To achieve these goals, dual connectivity (DC) is playing a more and more important role to support control/user-plane splitting, which enables maintaining fixed control channel connections for reliability. In this paper, we develop a tractable framework for the downlink SINR analysis of DC assisted HCN. Based on stochastic geometry model, the data-control joint coverage probabilities under multi-frequency and single-frequency tiering are derived, which involve quick integrals and admit simple closed-forms in special cases. Monte Carlo simulations confirm the accuracy of the expressions. It is observed that the increase in mobility robustness of DC is at the price of control channel SINR degradation. This degradation severely worsens the joint coverage performance under single-frequency tiering, proving multi-frequency tiering a more feasible networking scheme to utilize the advantage of DC effectively. Moreover, the joint coverage probability can be maximized by adjusting the density ratio of small cell and macro cell eNBs under multi-frequency tiering, though changing cell association bias has little impact on the level of the maximal coverage performance.