• Journal of Information Processing Systems
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• v.16 no.3
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• pp.541-556
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• 2020

Wireless networks have become integral to society as they provide mobility and scalability advantages. However, their disadvantage is that they cannot control the media, which makes them vulnerable to various types of attacks. One example of such attacks is the evil twin access point (AP) attack, in which an authorized AP is impersonated by mimicking its service set identifier (SSID) and media access control (MAC) address. Evil twin APs are a major source of deception in wireless networks, facilitating message forgery and eavesdropping. Hence, it is necessary to detect them rapidly. To this end, numerous methods using clock skew have been proposed for evil twin AP detection. However, clock skew is difficult to calculate precisely because wireless networks are vulnerable to noise. This paper proposes an evil twin AP detection method that uses a multiple-feature-based machine learning classification algorithm. The features used in the proposed method are clock skew, channel, received signal strength, and duration. The results of experiments conducted indicate that the proposed method has an evil twin AP detection accuracy of 100% using the random forest algorithm.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.11
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• pp.183-190
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• 2012

Conventional physical activity monitoring systems, which use accelerometers, global positioning system (GPS), heartbeats, or body temperature information, showed limited performances due to their own restrictions on measurement environment and measurable activity types. To overcome these limitations, we developed a portable exercise analysis system that can analyze aerobic exercises as well as isotonic exercises. For bioelectric signal acquisition during exercise, waist belt with two body contact electrodes was used. For exercise analysis, the measured signals were firstly divided into two signal groups with different frequency ranges which can represent respiration related signal and muscular motion related signal, respectively. After then, power values, differential of power values, and median frequency values were selected for feature values. Selected features were used as inputs of support vector machine (SVM) to classify the exercise types. For verification of statistical significance, ANOVA and multiple comparison test were performed. The experimental results showed 100% accuracy for classification of aerobic exercise and isotonic resistance exercise. Also, classification of aerobic exercise, isotonic resistance exercise, and hybrid types of exercise revealed 92.7% of accuracy.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.7C
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• pp.937-945
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• 2004

A binary decision model which is used to denoising has demerits to measure the precise ratio of signal to noise because of only a binary classification. To supplement these demerits, complex statistical model and undecimated wavelet transform are generally exploited. In this paper, we propose a noise reduction method using a multi-level decision model for measuring the ratio of noise in noisy image. The propose method achieves good denoising performance with orthogonal wavelet transform because the ratio of signal to noise can be calculated to multi-valued form. In simulation results, the proposed denoising method outperforms 0.1dB in the PSNR sense than the state of art denoising algorithms using orthogonal wavelet transform.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.8
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• pp.752-759
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• 2008

In this paper, we propose a high accurate geo-location system based on a single base station, where its location is obtained by Time-of-Arrival(ToA) and Direction-of-Arrival(DoA) of the radio signal. For estimating accurate ToA and DoA information, a MUltiple SIgnal Classification(MUSIC) is adopted. However, the estimation of ToA and DoA using MUSIC algorithm is a time-consuming process. The position tracking bias is occurred by the time delay caused by the estimation process. In order to mitigate the bias error, we propose the estimation method of the position tracking bias and compensate the location error produced by the time delay using the position tracking bias mitigation. For accurate self-localization of mobile robot, the Unscented Kalman Filter(UKF) with position tracking bias is applied. The simulation results show the efficiency and accuracy of the proposed geo-location system and the enhanced performance when the Unscented Kalman Filter is adopted for mobile robot application.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.3
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• pp.212-218
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• 2013

This paper presents an SVD(Singular Value Decomposition)-Pseudo Spectrum method for SAR (Synthetic Aperture Radar) imaging. The purpose of this work is to improve resolution and target separability of SAR images. This paper proposes SVD-Pseudo Spectrum method whose advantages are noise robustness, reduction of sidelobes and high resolution of spectral estimation. SVD-Pseudo Spectrum method uses Hankel Matrix of signal components and SVD (Singular Value Decomposition) method. In this paper, it is demonstrated that the SVD-Pseudo Spectrum method shows better performance than the matched filtering method and the conventional super-resolution based multiple signal classification (MUSIC) method in SAR image processing. The targets to be separated are modeled, and this modeled data is used to demonstrate the performance of algorithms.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.7
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• pp.813-822
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• 2012

Abnormal operating sounds radiated from a moving transfer robot in LCD (liquid crystal display) product lines have been used for the fault detection line of a robot instead of other source signals such as vibrations, acoustic emissions, and electrical signals. Its advantage as a source signal makes it possible to monitor the status of multiple faults by using only a microphone, despite a relatively low sensitivity. The wavelet packet transform for feature extraction and the artificial neural network for fault classification are employed. It can be observed that the abnormal operating sound is sufficiently useful as a source signal for the fault diagnosis of mechanical components as well as other source signals.

• Smart Structures and Systems
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• v.5 no.4
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• pp.329-344
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• 2009

Ultrasonic chaotic excitations combined with sensor prediction algorithms have shown the ability to identify incipient damage (loss of preload) in a bolted joint. In this study we examine a physical experiment on a single-bolt aluminum lap joint as well as a three-dimensional physics-based simulation designed to model the behavior of guided ultrasonic waves through a similarly configured joint. A multiple bolt frame structure is also experimentally examined. In the physical experiment each signal is imparted to the structure through a macro-fiber composite (MFC) patch on one side of the lap joint and sensed using an equivalent MFC patch on the opposite side of the joint. The model applies the waveform via direct nodal displacement and 'senses' the resulting displacement using an average of the nodal strain over an area equivalent to the MFC patch. A novel statistical classification feature is developed from information theory concepts of cross-prediction and interdependence. This damage detection algorithm is used to evaluate multiple damage levels and locations.

• Journal of Advanced Navigation Technology
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• v.26 no.3
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• pp.166-171
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• 2022

In this paper, we propose an algorithm to improve DoA(direction of arrival) estimation performance of the subspace-based method by generating high robustness correlation matrix of the signals incident on the uniformly linear array antenna. The existing subspace-based DoA estimation method estimates the DoA by obtaining a correlation matrix and dividing it into a signal subspace and a noise subspace. However, the component of the correlation matrix obtained from the low SNR and small number of snapshots inaccurately estimates the signal subspace due to the noise component of the antenna, thereby degrading the DoA estimation performance. Therefore a robust correlation matrix is generated by arranging virtual signal vectors obtained from the existing correlation matrix in a sliding manner. As a result of simulation using MUSIC and ESPRIT, which are representative subspace-based methods,, the computational complexity increased by less than 2.5% compared to the existing correlation matrix, but both MUSIC and ESPRIT based on RMSE 1° showed superior DoA estimation performance with an SNR of 3dB or more.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2012.06a
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• pp.59-61
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• 2012

TDOA 기법은 위치추정 기법의 하나로 간단한 구조와 높은 정확도를 가지는 장점으로 인해 실내측위, 군사, 의료 분야 등에 자주 사용된다. 본 논문에서는 MPM(Matrix Pencil Method)를 이용한 고분해능 TDOA 추정 기법을 제안한다. 제안된 기법은 기존의 교차상관을 이용한 TDOA 기법에 비교하여 높은 정확도를 가지며 CW(Continuous Wave)와 같은 협대역 신호에 적용이 가능하다. 또한 잘 알려진 고분해능 기법 중 하나인 MUSIC(Multiple Signal Classification)에서 공분산 행렬을 사용하는 것과 달리 수집된 데이터를 바로 행렬로 만들어 사용하므로 복잡성이 낮은 특징이 있다. 제안된 기법의 성능을 검증하기 위해 소프트웨어 시뮬레이션 통해 SNR에 따른 오차와 연산량 측면에서 MUSIC 기법과 비교하였다.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.11b
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• pp.699-704
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• 2002

A method using beamforming algorithm has been developed to measure oblique incidence reflection coefficients of sound absorption materials. MUSIC(Multiple Signal Classification) method detects the angles of incidence and reflection. By separating the incident and reflected waves using beamforming method, the reflection coefficient is calculated. Spatial smoothing technique is also used to reduce the coherence between the incident and reflected waves. The test materials were modeled as a locally reacting surface. Numerical and experiment results are performed to verify the acuracy of proposed method.