• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.3
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• pp.428-451
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• 2010

Traffic matrix-based anomaly detection and DDoS attacks detection in networks are research focus in the network security and traffic measurement community. In this paper, firstly, a new type of unidirectional flow called IF flow is proposed. Merits and features of IF flows are analyzed in detail and then two efficient methods are introduced in our DDoS attacks detection and evaluation scheme. The first method uses residual variance ratio to detect DDoS attacks after Recursive Least Square (RLS) filter is applied to predict IF flows. The second method uses generalized likelihood ratio (GLR) statistical test to detect DDoS attacks after a Kalman filter is applied to estimate IF flows. Based on the two complementary methods, an evaluation formula is proposed to assess the seriousness of current DDoS attacks on router ports. Furthermore, the sensitivity of three types of traffic (IF flow, input link and output link) to DDoS attacks is analyzed and compared. Experiments show that IF flow has more power to expose anomaly than the other two types of traffic. Finally, two proposed methods are compared in terms of detection rate, processing speed, etc., and also compared in detail with Principal Component Analysis (PCA) and Cumulative Sum (CUSUM) methods. The results demonstrate that adaptive filter methods have higher detection rate, lower false alarm rate and smaller detection lag time.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.2
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• pp.234-239
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• 2006

In this paper, we propose an interacting multiple model (IMM) method using intelligent tracking filter with fuzzy gain to reduce tracking errors for maneuvering targets. In the proposed filter, the unknown acceleration input for each sub-model is determined by mismatches between the modelled target dynamics and the actual target dynamics. After a acceleration input is detected, the state estimates for each sub-filter are modified. To modify the accurate estimation, we propose the fuzzy gain based on the relation between the filter residual and its variation. To optimize each fuzzy system, we utilize the genetic algorithm (GA). The tracking performance of the proposed method is compared with those of the adaptive interacting multiple model(AIMM) method and input estimation (IE) method through computer simulations.

• Journal of Korea Multimedia Society
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• v.6 no.7
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• pp.1131-1138
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• 2003

Active noise control(ANC) is an approach to noise reduction in which a secondary noise source destructively interferes with the unwanted noise is introduced. Generally, the performance of ANC is determined how well a secondary noise tracks noises. A secondary noise is generated from the cancelling speaker and a error sensor pick up error signal. The transfer function between the cancelling speaker and the error sensor is not flat and distorts secondary noises. Consequently, the performance of ANC is degraded by the transfer function. In this paper, a single sensor ANC which considers the characteristics of the speaker and the error sensor is proposed. To reduce distortion of secondary noises, the transfer function is estimated by adaptive inverse modelling and the primary noises are estimated by Kalman filter. Experimental results show that the proposed single sensor ANC effectively attenuates noises.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.425-428
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• 2006

In this paper, we propose an interacting multiple model (IMM) method using intelligent tracking filter with fuzzy gain to reduce tracking errors for maneuvering targets. In the proposed filter, to exactly estimate for each sub-model, we propose the fuzzy gain based on the relation between the filter residual and its variation. To optimize each fuzzy system, we utilize the genetic algorithm (GA). Finally, the tracking performance of the proposed method is compared with those of the adaptive interacting multiple model (AIMM) method and input estimation (IE) method through computer simulations.

• Journal of Advanced Navigation Technology
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• v.3 no.2
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• pp.156-166
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• 1999

In multi-vehicle tracking systems Kalman filter is generally used for tracking vehicles. Despite well known advantages of Kalman filter that presents optimality with constraints, it is difficult to track several vehicles in real time simultaneously due to a large number of computations. In this paper, we propose a multi-vehicle tracking system with an adaptive predictor that employs recursive least square algorithm which can be easily implemented for real time application on a transversal filter. The performance of the proposed tracking system is compared to one with Kalman filter using a synthetic sequential image generated by computer graphics and real sequential image taken at intersections. Simulation results show that the proposed tracking system can be applied to track vehicles in real sequential image at the rate of 30 frame/sec on a PC environments without any special hardwares.

• Journal of Sensor Science and Technology
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• v.28 no.2
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• pp.81-87
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• 2019

One of the major issues in inertial and magnetic measurement unit (IMMU)-based 3D orientation estimation is compensation for magnetic disturbances in magnetometer signals, as the magnetic disturbance is a major cause of inaccurate yaw estimation. In the proposed approach, a kinematic constraint is used to provide a measurement equation in addition to the accelerometer and magnetometer signals to mitigate the disturbance effect on the orientation estimation. Although a Kalman filter (KF) is the most popular framework for IMMU-based orientation estimation, a complementary filter (CF) has its own advantages over KF in terms of mathematical simplicity and ease of implementation. Accordingly, this paper introduces a quaternion-based CF with a constraint-combined correction equation. Furthermore, the weight of the constraint relative to the magnetometer signal is adjusted to adapt to magnetic environments to optimally deal with the magnetic disturbance. In the results of our validation experiments, the average and maximum of yaw errors were $1.17^{\circ}$ and $1.65^{\circ}$ from the proposed CF, respectively, and $8.88^{\circ}$ and $14.73^{\circ}$ from the conventional CF, respectively, showing the superiority of the proposed approach.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.12
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• pp.59-66
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• 1993

In this paper we propose an adaptive update rate tracking algorithm for a phased array radar, based on the interacting multiple model(IMM) algorithm. The purpose of the IMM algorithm hers is twofold: 1) to estimate and predict the target states, and 2) to estimate the level of the process noise. Using the estimate of the process noise level adapted to target dynamics, the update interval is determined to maintain a desired prediction accuracy so that the radar system load is minimized. The adaptive update rate tracking algorithm is implemented for a phased array radar and evaluated with Monte Carlo simulations on various trajectories. The evaluation results of the proposed algorithm and a standard Kalman filter without the adaptive update rate control are presented to compare.

• Smart Structures and Systems
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• v.32 no.1
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• pp.9-21
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• 2023

The parameters of civil engineering structures have time-variant characteristics during their service. When extremely large external excitations, such as earthquake excitation to buildings or overweight vehicles to bridges, apply to structures, sudden or gradual damage may be caused. It is crucially necessary to detect the occurrence time and severity of the damage. The unscented Kalman filter (UKF), as one efficient estimator, is usually used to conduct the recursive identification of parameters. However, the conventional UKF algorithm has a weak tracking ability for time-variant structural parameters. To improve the identification ability of time-variant parameters, an adaptive UKF with forgetting factor (AUKF-FF) algorithm, in which the state covariance, innovation covariance and cross covariance are updated simultaneously with the help of the forgetting factor, is proposed. To verify the effectiveness of the method, this paper conducted two case studies as follows: the identification of time-variant parameters of a simply supported bridge when the vehicle passing, and the model updating of a six-story concrete frame structure with field test during the Yangbi earthquake excitation in Yunnan Province, China. The comparison results of the numerical studies show that the proposed method is superior to the conventional UKF algorithm for the time-variant parameter identification in convergence speed, accuracy and adaptability to the sampling frequency. The field test studies demonstrate that the proposed method can provide suggestions for solving practical problems.

• Smart Structures and Systems
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• v.31 no.3
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• pp.259-273
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• 2023

Real-time hybrid simulation (RTHS) is an effective experimental technique for structural dynamic assessment. However, time delay causes displacement de-synchronization at the interface between the numerical and physical substructures, negatively affecting the accuracy and stability of RTHS. To this end, the authors have proposed a model-based adaptive control strategy with a Kalman filter (MAC-KF). In the proposed method, the time delay is mainly mitigated by a parameterized feedforward controller, which is designed using the discrete inverse model of the control plant and adjusted using the KF based on the displacement command and measurement. A feedback controller is employed to improve the robustness of the controller. The objective of this study is to further validate the power of dealing with a nonlinear control plant and to investigate the potential challenges of the proposed method through actual experiments. In particular, the effect of the order of the feedforward controller on tracking performance was numerically investigated using a nonlinear control plant; a series of actual RTHS of a frame structure equipped with a magnetorheological damper was performed using the proposed method. The findings reveal significant improvement in tracking accuracy, demonstrating that the proposed method effectively suppresses the time delay in RTHS. In addition, the parameters of the control plant are timely updated, indicating that it is feasible to estimate the control plant parameter by KF. The order of the feedforward controller has a limited effect on the control performance of the MAC-KF method, and the feedback controller is beneficial to promote the accuracy of RTHS.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.2
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• pp.39-49
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• 2002

This paper proposes the MAWUPC (Motion Adaptive Weighted Unmatched Pixel Count) algorithm to track multiple objects of similar color The MAWUPC algorithm has the new method that combines color and motion effectively. We apply the MAWUPC algorithm to face and hand tracking against complex background in an image sequence captured by using single camera. The MAWUPC algorithm is an improvement of previously proposed AWUPC (Adaptive weighted Unmatched Pixel Count) algorithm based on the concept of the Moving Color that combines effectively color and motion information. The proposed algorithm incorporates a color transform for enhancing a specific color, the UPC(Unmatched Pixel Count) operation for detecting motion, and the discrete Kalman filter for reflecting motion. The proposed algorithm has advantages in reducing the bad effect of occlusion among target objects and, at the same time, in rejecting static background objects that have a similar color to tracking objects's color. This paper shows the efficiency of the proposed MAWUPC algorithm by face and hands tracking experiments for several image sequences that have complex backgrounds, face-hand occlusion, and hands crossing.