• International journal of advanced smart convergence
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• v.7 no.3
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• pp.101-109
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• 2018

In this paper, we propose an adaptive filtering scheme of connection requests for the defense of malicious energy consumption attacks against wireless computing devices with limited energy budget. The energy consumption attack tries to consume the battery energy of a wireless device with repeated connection requests and shut down the wireless device by exhausting its energy budget. The proposed scheme blocks a connection request of the energy consumption attack in the middle, if the same connection request is repeated and its request result is failed continuously. In order to avoid the blocking of innocuous mistakes of normal users, the scheme gives another chance to allow connection request after a fixed blocking time. The scheme changes the blocking time adaptively by comparing the message arriving ate during non-blocking period and that during blocking period. Evaluation shows that the proposed defense scheme saves up to 94% energy consumption compared to the non-defense case.

• Journal of Sensor Science and Technology
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• v.28 no.3
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• pp.146-151
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• 2019

This paper reports a sliding window filtering approach for ground moving targets with cross-correlated sensor noise and uncertainty. In addition, the effect of uncertain parameters during a tracking error on the model performance is considered. A distributed fusion sliding window filter is also proposed. The distributed fusion filtering algorithm represents the optimal linear combination of local filters under the minimum mean-square error criterion. The derivation of the error cross-covariances between the local sliding window filters is the key to the proposed method. Simulation results of the motion of the ground moving target a demonstrate high accuracy and computational efficiency of the distributed fusion sliding window filter.

• The Journal of the Acoustical Society of Korea
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• v.15 no.3E
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• pp.4-12
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• 1996

In this study, we examine the applicability of an artificial neural network(ANN) for filtering underwater random noise and for identifying underlying signals taken from noisy environment. The approach is to find a way of compressing the input data and then decompressing it using an ANN as in image compressing process. It is well known that random signal is hard to compress while ordered information is not. The use of a limited number of processing elements(PEs) in the hidden layer of an Ann ensures that some of the noise would be removed in the reconstruction process. Two types of the signals, synthesized and measured, are used to examine the effectiveness of the ANN-based filter. After training process is completed, the ANN successfully extracts the underlying signals form the synthesized or measured noisy signals. In particular, compared with the results form without filtering or moving averaged, the ANN-based filter gives much better spectrograms to identify underlying signals from the measured noisy data. This filtering process is achieved without using and kind of highly accurate signal processing technique. More experimentation needs to be followed to develop the ANN-based filtering technique to the level of complete understanding.

• Journal of the Korea Institute of Military Science and Technology
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• v.19 no.4
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• pp.500-507
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• 2016

In order to increase the survivability of combatant ship, measuring and analyzing the infrared radiation is important. Consequently, providing analysis report is also important for the progress of the new combatant ship design. This paper proposes a design and software implementation of filtering for the noise reduction of mid-wave IR camera image. We reduced the total test cost by using the suggested software filtering technique instead of hardware replacement or re-calibration. In addition, we enhanced the accuracy of analysis results by adjusting the parameters of software filtering according to the results of filtered image.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.491-494
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• 2006

Kalman filtering (KF) is hard to be applied to the SINS (Strap-down Inertial Navigation System)/RDSS (Radio Determination Satellite Service) integrated navigation system directly because the time delay of RDSS positioning in active mode is random. BP (Back-Propagation) Neuron computing as a powerful technology of Artificial Neural Network (ANN), is appropriate to solve nonlinear problems such as the random time delay of RDSS without prior knowledge about the mathematical process involved. The new algorithm betakes a BP neural network (BPNN) and velocity feedback to aid KF in order to overcome the time delay of RDSS positioning. Once the BP neural network was trained and converged, the new approach will work well for SINS/RDSS integrated navigation. Dynamic vehicle experiments were performed to evaluate the performance of the system. The experiment results demonstrate that the horizontal positioning accuracy of the new approach is 40.62 m (1 ${\sigma}$), which is better than velocity-feedback-based KF. The experimental results also show that the horizontal positioning error of the navigation system is almost linear to the positioning interval of RDSS within 5 minutes. The approach and its anti-jamming analysis will be helpful to the applications of SINS/RDSS integrated systems.

• Journal of Positioning, Navigation, and Timing
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• v.4 no.4
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• pp.181-186
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• 2015

A High-degree Cubature Kalman Filter (CKF) is proposed to deal with the Strapdown Inertial Navigation System (SDINS) alignment problem. In-flight Alignment (IFA) is an effective method to compensate for attitude errors of the navigation system. While providing precise attitude error compensation, however, the external source aided alignment often creates a nonlinear filtering problem caused by a large misalignment angle. Introduced recently, Cubature Kalman Filter is a suitable technique for various nonlinear problems. In this paper, a higher degree CKF is applied to this accuracy-is-everything SDINS IFA problem. The simulation results show that the proposed technique outperformed a traditional nonlinear filter in terms of precision and alignment time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.11
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• pp.4170-4188
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• 2014

In this study, we present a denoising algorithm for high-frame-rate videos in an ultra-low illumination environment on the basis of Kalman filtering model and a new motion segmentation scheme. The Kalman filter removes temporal noise from signals by propagating error covariance statistics. Regarded as the process noise for imaging, motion is important in Kalman filtering. We propose a new motion estimation scheme that is suitable for serious noise. This scheme employs the small motion vector characteristic of high-frame-rate videos. Small changing patches are intentionally neglected because distinguishing details from large-scale noise is difficult and unimportant. Finally, a spatial bilateral filter is used to improve denoising capability in the motion area. Experiments are performed on videos with both synthetic and real noises. Results show that the proposed algorithm outperforms other state-of-the-art methods in both peak signal-to-noise ratio objective evaluation and visual quality.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.05a
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• pp.745-748
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• 2003

HLA-RTI is Middleware for the distribute simulation that developed in the US Department of Defense. This provides fast accomplishment speed and reliability than distribute simulation Middleware by transfer. However, DDM(Data Distribution Management) service is used as data filtering technology in the existing HLA-RTI. As for this, the problem that network traffic increases in data exchange between the mobility simulation objects is generated. it proposes applying agent technology to the mobility simulation object in order to solve these problems in this paper in this. And this paper applies that to practical simulation and analyzes performance between each data filtering technology with comparison.

• Journal of Internet Computing and Services
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• v.23 no.1
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• pp.125-134
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• 2022

The development of information communication and artificial intelligence technology requires the intelligent command and control (C2) system for Korean military, and various studies are attempted to achieve it. In particular, as a volume ofinformation in the C2 workflow increases exponentially, this study pays attention to the collaborative filtering (CF) and recommendation systems (RS) that can provide the essential information for the users of the C2 system has been developed. The RS performing information filtering in the C2 system should provide an explanatory recommendation and consider the context of the tasks and users. In this paper, we propose a contextual pre-filtering CARS framework that recommends information in the C2 workflow. The proposed framework consists of four components: 1) contextual pre-filtering that filters data in advance based on the context and relationship of the users, 2) feature selection to overcome the data sparseness that is a weak point for the CF, 3) the proposed CF with the features distances between the users used to calculate user similarity, and 4) rule-based post filtering to reflect user preferences. In order to evaluate the superiority of this study, various distance methods of the existing CF method were compared to the proposed framework with two experimental datasets in real-world. As a result of comparative experiments, it was shown that the proposed framework was superior in terms of MAE, MSE, and MSLE.

• Journal of the Korea Institute of Military Science and Technology
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• v.17 no.3
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• pp.350-357
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• 2014

MRDS is a short range missile/rocket defense system which protects a main battle tank(MBT) from threats at a short range. It is composed of 2 radars, 2 infrared trackers(IRT)s, 1 fire control computer(FCC), 2 launchers and countermeasures. In this paper, the radar and the IRT models based on sensing errors, the FCC model based on filtering errors, the launcher model based on driving errors and the countermeasure model based on flying errors are proposed to analyze the defense performance with the approaching RPG-7 and the moving MBT. The simulation results are presented to evaluate and verify the effectiveness of the proposed method.