• The Journal of the Acoustical Society of Korea
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• v.36 no.4
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• pp.229-237
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• 2017

In this paper we propose an auto tonal detection method which exploits short term stationary when targets located in a detection beam area and then additional methods are proposed in order to reduce the computational complexity of the proposed method. The proposed method is adaptive to input signals and robust against interference caused by multiple targets because it compares an expected value of input signals with a threshold value which are estimated from a single beam while signals are keep stationary. The performances of the proposed methods are evaluated using by simulated data and acquired data from real ocean. The proposed method has shown better performance than conventional CFAR (Constant False Alarm Rate) methods.

• The Journal of the Acoustical Society of Korea
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• v.19 no.8
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• pp.78-83
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• 2000

Passive sonar system is designed to classify the underwater targets by analyzing and comparing the various acoustic characteristics such as signal strength, bandwidth, number of tonals and relationship of tonals from the extracted tonals and frequency lines. First of all the precise detection and extraction of signal frequency lines is of particular importance for enhancing the reliability of target classification. But, the narrowband frequency lines which are the line formed in spectrogram by a tonal of constant frequency in each frame can be detected weakly or discontinuously because of the variation of signal strength and transmission loss in the sea. Also, it is very difficult to detect and extract precisely the signal frequency lines by the complexity of impulsive ambient noise and signal components. In this paper, the automatic detection and extraction method that can detect and extract the signal components of frequency tines precisely are proposed. The proposed method can be applied under the bad conditions with weak signal strength and high ambient noise. It is confirmed by the simulation using real underwater target data.

• Journal of the Korea Society for Simulation
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• v.21 no.2
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• pp.79-90
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• 2012

In this paper, a feature signal extraction method is proposed in order to enhance the low performance of fault detection caused by unbalanced data which denotes the situations when severe disparity exists between the numbers of class instances. Most of the cyclic signals gathered during the process are recognized as normal, while only a few signals are regarded as fault; the majorities of cyclic signals data are unbalanced data. SOM(Self-Organizing Map)-based feature signal extraction method is considered to fix the adverse effects caused by unbalanced data. The weight neurons, mapped to the every node of SOM grid, are extracted as the feature signals of both class data which are used as a reference data set for fault detection. kNN(k-Nearest Neighbor) and SVM(Support Vector Machine) are considered to make fault detection models with comparisons to Hotelling's $T^2$ Control Chart, the most widely used method for fault detection. Experiments are conducted by using simulated process signals which resembles the frequent cyclic signals in semiconductor manufacturing.

• Journal of Internet Computing and Services
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• v.5 no.2
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• pp.57-67
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• 2004

In this paper we present the architecture of intrusion detection system that enhances the performance of system and the correctness of intrusion detection. A network intrusion is usually composed of several steps of action taken by the attackers. Each action in the steps can be characterized by its signature. But normal and non-intrusive action can also include the same signature, It can result in incorrect detection. The presented system uses the correlation of series of signatures that consist of an intrusion. So Its decision on an intrusion is highly reliable. And variations of known intrusions can easily be detected without any knowledge of the variations.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.75-76
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• 2012

감시정찰 센서네트워크 시스템에서 사용하는 센서 신호 중 진동 신호는 무인정찰 시스템상에서 침입자의 발자국 신호와 차량의 움직임을 탐지하는데 유용하게 사용 된다. 특히 발자국 진동 신호는 특유의 impulsive 한 성질로 인해 다른 진동 신호와 구별이 용이하며 많은 훈련이 필요한 통계적 모델 링 대신 단순한 Pitch-Based 중점 탐지 기법을 도입하여 탐지 및 분류가 가능하고 이를 통해 보다 실시간 환경에 적합한 시스템을 구현할 수 있다.

• Journal of the Korea Society for Simulation
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• v.31 no.4
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• pp.11-22
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• 2022

Unlike a short-range, a long-range underwater acoustic communication(UWAC) uses low frequency signal and deep sound channel to minimize propagation loss. In this case, even though communication signals are modulated using a covert transmission technique such as spread spectrum, it is hard to conceal the existence of the signals. The unconcealed communication signal can be utilized as active sonar signal by enemy and presence of underwater vehicles may be exposed to the interceptor. Since it is very important to maintain stealthiness for underwater vehicles, the detection probability of friendly underwater vehicles should be considered when interceptor utilizes our long-range UWAC signal. In this paper, we modeled a long-range UWAC environment for analyzing the detection performance of underwater vehicles and proposed the region of interest(ROI) setup method and the measurement of detection performance. By computer simulations, we yielded parameters, analyzed the detection probability and the detection performance in ROI. The analysis results showed that the proposed detection performance analysis method for underwater vehicles could play an important role in the operation of long-range UWAC equipment.

• Journal of Satellite, Information and Communications
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• v.12 no.4
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• pp.26-33
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• 2017

A drone performs a mission through remote control or automatic control, which uses wireless communications technology. Recently the increasing use of drones, the drone signal RF detection is necessary. In this paper, we propose an efficient dron RF detection method through simulations considering Wi-Fi, Bluetooth and dedicated protocol dron communication method in ISM(Industry Science Medical) band.. After configuring an environment where a common terminal and a drone signal are mixed, a general terminal and a drone signal are distinguished from each other by using a RF characteristic according to a dron movement. The proposed drone RF detection method is the WRMD(Windowed RSSI Moving Detection) operation and the Doppler frequency identification method. The simulation environments consist to mixed for two signals and four signals. We analysis the performance to proposed drone RF detection technique thorough detection rate.

• Journal of the Korean Data and Information Science Society
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• v.22 no.2
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• pp.197-206
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• 2011

The ability to detect online abnormal events in signals is essential in many real-world signal processing applications. In order to detect abnormal events, previously known algorithms require an explicit signal statistical model, and interpret abnormal events as statistical model abrupt changes. In general, maximum likelihood and Bayesian estimation theory to estimate well as detection methods have been used. However, the above-mentioned methods for robust and tractable model, it is not easy to estimate. More freedom to estimate how the model is needed. In this paper, we investigate a machine learning, descriptor-based approach that does not require a explicit descriptors statistical model, based on support vector machines are known to be robust statistical models and a sequential optimal algorithm online support vector machine is introduced.

• Review of KIISC
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• v.31 no.1
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• pp.15-23
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• 2021

자율주행 시스템이 탑재되어 있는 무인이동체는 운용환경에 따라 공중, 해상, 육상 무인이동체로 분류할 수 있고 모든 분야에서 관련 기술 개발이 활발히 진행되고 있다. 무인이동체는 자율주행 시스템이 탑재되어 외부 환경을 스스로 인식해 상황을 판단하는 특징을 갖고 있다. 따라서, 무인이동체는 센서로부터 수집되는 데이터를 이용하여 주변 환경을 인식해야 한다. 이러한 이유로 보안 (Security) 분야에서는 무인이동체에 탑재되는 센서를 대상으로 신호 오류주입을 수행하여 해당 무인이동체의 오동작을 유발하는 연구결과들이 최근 발표되고 있다. 신호 오류주입공격은 물리레벨 (PHY-level) 에서 수행되기 때문에, 공격 수행 여부를 소프트웨어 레벨에서 탐지하는 것은 매우 어렵다는 특징을 갖고 있다. 현재까지 신호 오류주입 공격을 탐지할 수 있는 방법은 다수의 센서를 이용하는 센서퓨전 (Sensor Fusion)을 기반으로 하는 방법이 있다. 하지만, 현실적으로 하나의 무인이동체에 동일한 기능을 하는 센서 여러 개를 중복해서 탑재하는 것은 어려움이 있다. 그리고 단일 센서만을 이용하여 신호 오류주입 공격을 탐지하는 방법에 대해서는 아직까지 연구가 진행되고 있지 않다. 본 논문에서는 무인이동체 환경에서 가장 널리 사용되고 있는 MEMS 센서를 대상으로 신호 오류주입 공격을 재연하고, 단일 센서 환경에서 해당 공격을 탐지할 수 있는 방법에 대하여 제안한다.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.10
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• pp.783-790
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• 2018

The frequency-hopping technique is one of the spread-spectrum techniques. Frequency hopping is a communication system in which the carrier frequency channel is hopped within the wideband. Therefore, a frequency-hopping system has such advantages as antijamming and low probability of intercept. This system is often used in military communications. Because frequency-hopping signal detection is difficult, it is an important research issue. A novel detection technique is proposed that can improve detection probability. When the received signal is transformed to a frequency domain sample by fast Fourier transform, spectral leakage lowers the detection probability. This problem can be solved by using the Hamming window, and the detection probability can be increased. However, in a frequency-hopping environment, the windowing technique lowers the detection probability. The proposed method solves this weakness. The simulation results show that the proposed detection technique improves the detection probability by as much as 13 %.