• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2589-2609
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• 2021

Segment routing (SR) is a highly implementable approach for traffic engineering (TE) with high flexibility, high scalability, and high stability, which can be established upon existing network infrastructure. Thus, when a network failure occurs, it can leverage the existing rerouting methods, such as rerouting based on Interior Gateway Protocol (IGP) and fast rerouting with loop-free alternates. To better exploit these features, we propose a high-performance and easy-to-deploy method SRUF (Segment Routing under Uncertain Failures). The method is inspired by the Value-at-Risk (VaR) theory in finance. Just as each investment risk is considered in financial investment, SRUF also considers each traffic distribution scheme's risk when forwarding traffic to achieve optimal traffic distribution. Specifically, SRUF takes into account that every link may fail and therefore has inherent robustness and high availability. Also, SRUF considers that a single link failure is a low-probability event; hence it can achieve high performance. We perform experiments on real topologies to validate the flexibility, high-availability, and load balancing of SRUF. The results show that when given an availability requirement, SRUF has greater load balancing performance under uncertain failures and that when given a demand requirement, SRUF can achieve higher availability.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.44 no.8
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• pp.23-32
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• 2007

We propose a group-based security scheme for hierarchical wireless sensor networks. We model the network for secure routing with 3-tier sensor network comprised of three types of nodes: Base Station, Group Dominator and ordinary Sensor Nodes. Group-based deployment is performed using Gaussian (normal) distribution and show that more than 85% network connectivity can be achieved with the proposed model. The small groups with pre-shared secrets form the secure groups where group dominators form the backbone of the entire network. The scheme is devised for dealing with sensory data aggregated by groups of collocated sensors; i.e., local sensed data are collected by the dominating nodes and sent an aggregated packet to the base station via other group dominators. The scheme is shown to be light-weight, and it offers a stronger defense against node capture attacks. Analysis and simulation results are presented to defend our proposal. Analysis shows that robustness can significantly be improved by increasing the deployment density using both the dominating and/or ordinary sensor nodes.

• Journal of the Korea Institute of Military Science and Technology
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• v.24 no.3
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• pp.264-271
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• 2021

Raman spectroscopy is an equipment that is widely used for classifying chemicals in chemical defense operations. However, the classification performance of Raman spectrum may deteriorate due to dark current noise, background noise, spectral shift by vibration of equipment, spectral shift by pressure change, etc. In this paper, we compare the classification accuracy of various machine learning algorithms including k-nearest neighbor, decision tree, linear discriminant analysis, linear support vector machine, nonlinear support vector machine, and convolutional neural network under noisy and spectral shifted conditions. Experimental results show that convolutional neural network maintains a high classification accuracy of over 95 % despite noise and spectral shift. This implies that convolutional neural network can be an ideal classification algorithm in a real combat situation where there is a lot of noise and spectral shift.

• Proceedings of the Korean Geotechical Society Conference
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• 2010.09a
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• pp.263-268
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• 2010

Thermal characterization of geomaterials has significant implication on the geothermal energy, disposal of nuclear wastes, geological sequestration of carbon dioxides and recovery of hydrocarbon resources. Heat transfer in multiphase materials is dominated by the thermal conductivity of consisting components, porosity, degree of saturation and overburden pressure, which have been investigated by the empirical correlation at macro-scale. The thermal measurement by Transient Plane Source (TPS) and associated algorithm for interpretation of thermal behavior in geomaterials corroborate the robustness of sensing techniques. The method simultaneously provides thermal conductivity, diffusivity and volumetric heat capacity. The newly introduced thermal network model enables estimating thermal conductivity of geomaterials subjected to the effective stress, which has not been evaluated using previous thermal models. The proposed methods shows the applicability of reliability of TPS technique and thermal network model.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.12
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• pp.84-91
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• 2004

A CCD camera with a laser stripe was applied to realize the automatic weld seam tracking in GMAW. It takes relatively long time to process image on-line control using the basic Hough transformation, but it has a tendency of robustness over the noises such as spatter and arc tight. For this reason, it was complemented with adaptive Hough transformation to have an on-line processing ability for scanning specific weld points. The adaptive Hough transformation was used to extract laser stripes and to obtain specific weld points. The 3-dimensional information obtained from the vision system made it possible to generate the weld torch path and to obtain the information such as width and depth of weld line. In this study, a neural network based on the generalized delta rule algorithm was adapted for the process control of GMA, such as welding speed, arc voltage and wire feeding speed.

• Journal of Korea Multimedia Society
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• v.13 no.6
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• pp.890-900
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• 2010

The Bayesian networks methods provide an efficient tool for performing information fusion and decision making under conditions of uncertainty. This paper proposes Bayes estimators for the system effectiveness in energy saving of the wireless sensor networks by use of the Bayesian method under the non-informative prior knowledge about means of active and sleep times based on time frames of sensor nodes in a wireless sensor network. And then, we conduct a case study on some Bayesian estimation models for the system energy saving effectiveness of a wireless sensor network, and evaluate and compare the performance of proposed Bayesian estimates of the system effectiveness in energy saving of the wireless sensor network. In the case study, we have recognized that the proposed Bayesian system energy saving effectiveness estimators are excellent to adapt in evaluation of energy efficiency using non-informative prior knowledge from previous experience with robustness according to given values of parameters.

• The Journal of Korea Robotics Society
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• v.4 no.4
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• pp.312-319
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• 2009

This paper proposes a low-complexity indoor localization method of mobile robot under the dynamic environment by fusing the landmark image information from an ordinary camera and the distance information from sensor nodes in an indoor environment, which is based on sensor network. Basically, the sensor network provides an effective method for the mobile robot to adapt to environmental changes and guides it across a geographical network area. To enhance the performance of localization, we used an ordinary CCD camera and the artificial landmarks, which are devised for self-localization. Experimental results show that the real-time localization of mobile robot can be achieved with robustness and accurateness using the proposed localization method.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.54 no.1
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• pp.8-14
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• 2005

This paper is proposed hybrid artificial intelligent(HAI) controller for speed control of surface permanent magnet synchronous motor(SPMSM) drive. The design of this algorithm based on HAI controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the HAI controller is evaluated by analysis for various operating conditions. The results of analysis prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.315-317
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• 2006

In this paper, we design a terminal sliding mode controller based on neural network for nonlinear systems with uncertainties. Terminal sliding mode control (TSMC) method can drive the tracking errors to zero within finite time. Also, TSMC has the advantages such as improved performance, robustness, reliability and precision by contrast with classical sliding mode control. For the control of nonlinear system with uncertainties, we employ the self-recurrent wavelet neural network(SRWNN) which is used for the prediction of uncertainties. The weights of SRWNN are trained by adaptive laws based on Lyapunov stability theorem. Finally, we carry out simulations to illustrate the effectiveness of the proposed control.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.151-155
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• 1996

To automate an excavator the control issues resulting from environmental uncertainties must be solved. In particular the interactions between the excavation tool and the excavation environment are dynamic, unstructured and complex. In addition, operating modes of an excavator depend on working conditions, which makes it difficult to derive the exact mathematical model of excavator. Even after the exact mathematical model is established, it is difficult to design of a controller because the system equations are highly nonlinear and the state variable are coupled. The objective of this study is to design a multi-layer neural network which controls the position of excavator's attachment. In this paper, a dynamic controller has been developed based on an error back-propagation(BP) neural network. Computer simulation results demonstrate such powerful characteristics of the proposed controller as adaptation to changing environment, robustness to disturbance and performance improvement with the on-line learning in the position control of excavator attachment.