• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.4
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• pp.537-543
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• 2003

Transient vibration generated by developing localized fault in gear can be used as indicators in gear fault detection. This vibration signal suffers from the background noise such as gear meshing frequency and its harmonics and broadband noise. Thus in order to extract the information about the only gear fault from the raw vibration signal measured on the gearbox this signal is processed to reduce the background noise with many kinds of signal-processing tools. However, these signal-processing tools are often very complex and time waste. Thus. in this paper. we propose a novel approach detecting the damage of gearbox and analyzing its pattern using the raw vibration signal. In order to do this, the residual signal. which consists of the sideband components of the gear meshing frequent) and its harmonics frequencies, is extracted from the raw signal by the power spectral density (PSD) to obtain the information about the fault and is used as the input data of the artificial neural network (ANN) for analysis of the pattern of gear fault. This novel approach has been very successfully applied to the damage analysis of a laboratory gearbox.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.8C
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• pp.1025-1033
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• 2004

This paper covers the detection of covert direct sequence spread spectrum signal without the PN(Pseudo Noise) code information. Due to its low probability of interception, the difficulty of spectrum surveillance increases. Detection parameters are the signal existence of given bandwidth, the length of spreading sequence used by transmitter, and the identification of spreading code for detected chip length. The triple correlation function(TCF) value which is one of the higher order statistical signal processing techniques can be used to detect spread spectrum signal without a prior knowledge, but, it has weakness that TCF results depend on the spread data sequence in actual application. This paper proposes the new scheme that not only overcomes the weakness but also presents better performance than the traditional TCF scheme. The performance comparison of conventional TCF with proposed technique shows that the triple correlation estimator(TCE) has better detection capability.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.2
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• pp.101-111
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• 2023

Many spoofing detection studies have been conducted to cope with the most difficult types of deception among various disturbances of GPS, such as jamming, spoofing, and meaconing. In this paper, we propose a spoofing detection scheme based on elevation masked-relative received power between GPS L1 and L2 signals in a system using a multi-band array antenna. The proposed scheme focuses on enabling spoofing to be normally detected and minimizes the possibility of false detection in an environment where false alarms may occur due to pattern distortion among elements of an array antenna. The pattern distortion weakens the GPS signal strength at low elevation. It becomes confusing to detect a spoofing signal based on the relative power difference between GPS L1 and L2, especially when GPS L2 has weak signal strength. We propose design parameters for the relative power threshold including beamforming gain, the minimum received power difference between L1 and L2, and the patch antenna gain difference between L1 and L2. In addition, in order to eliminate the weak signal strength of GPS L2 in the spoofing detection process, we propose a rotation matrix that sets the elevation mask based on platform coordinates. Array antennas generally do not have high usefulness in commercial areas where receivers are operated alone, but are considered essential in military areas where GPS receivers are used together with signal processing for beamforming in the direction of GPS satellites. Through laboratory and live sky tests using the device under test, the proposed scheme with an elevation mask detects spoofing signals well and reduces the probability of false detection relative to that without the elevation mask.

• 한국연소학회:학술대회논문집
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• 2002.11a
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• pp.223-229
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• 2002

Spark plug ionization signal could be useful in an internal combustion engine as a feedback signal for combustion diagnostics such as misfire detection, knocking detection and lambda control, but the signal has high level of cyclic fluctuation in an internal combustion engine due to residual gas, pressure, temperature, mixture composition in the spark gap. Because of this reason it is very difficult to apply ion signal to commercial engine control. In this Study, a correlation between A/F and spark plug ionization signal was studied in a constant volume chamber. Constant volume chamber with gas phase fuel(Propane) has homogeneous fuel composition , no mixture flow, same pressure and temperature on each test. The results show that mean chemi-ion signal has the highest correlation with A/F and intial pressure change has on effect on the thermal-ion signal and not on chemi-ion signal.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.8
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• pp.665-670
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• 2016

This paper proposes a repeat-back jamming signal detection scheme using a correlation ratio test metric of a combined pseudo-random noise signal for global navigation satellite systems. The correlation ratio test metric allows for the monitoring of possible distortions in the signal correlation. The proposed scheme is a modified version of the correlation ratio test metric to detect a repeat-back jamming signal in a multipath environment. Through a Monte-Carlo simulation, it is confirmed that the proposed scheme detects almost the whole case, which is received a repeat-back jamming signal under the 6 dB jamming to signal power ratio.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.4
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• pp.506-516
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• 1998

In this paper, an algorithm is proposed to detect the malfunction of plasma-etching characteristics using EPD signal trajectories. EPD signal trajectories offer many information on plasma-etching process state, so they must be considered as the most important data sets to predict the wafer states in plasma-etching process. A recent work has shown that EPD signal trajectories were successfully incorporated into process modeling through critical parameter extraction, but this method consumes much effort and time. So Principal component analysis(PCA) can be applied. PCA is the linear transformation algorithm which converts correlated high-dimensional data sets to uncorrelated low-dimensional data sets. Based on this reason neural network model can improve its performance and convergence speed when it uses the features which are extracted from raw EPD signals by PCA. Wafer-state variables, Critical Dimension(CD) and uniformity can be estimated by simulation using neural network model into which EPD signals are incorporated. After CD and uniformity values are predicted, proposed algorithm determines whether malfunction values are produced or not. If malfunction values arise, the etching process is stopped immediately. As a result, through simulation, we can keep the abnormal state of etching process from propagating into the next run. All the procedures of this algorithm can be performed on-line, i.e. wafer to wafer.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.10 no.3
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• pp.123-129
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• 1985

This paper describes the determination of threshold value in order to determine the presence of absence of weak signal with SNR of 0 dB in 100kHz bandwidth. As a detection method, it has been used a recent LOC structure fitting for detecting weak signal in stead of a conventional method like Neyman-Peason crtical criterion. The signal for detection is the OOK modulation signal used in data and morse code transmission. The non-Gaussian noise similar to Laplacian type has been chosen in transmission path. As a result of experiment, comparing probability of detection by one critical point with that by two critical points with fixing as arbitrary false alarm probability, we have found that method has been shown to be better than the conventional method.

• Journal of the Institute of Convergence Signal Processing
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• v.7 no.4
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• pp.178-183
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• 2006

As a lot of researches on the speech signal processing are performed due to the recent rapid development of the information-communication technology. the pitch period is used as an important element to various speech signal application fields such as the speech recognition. speaker identification. speech analysis. or speech synthesis. A variety of algorithms for the time and the frequency domains related with such pitch period detection have been suggested. One of the pitch detection algorithms for the time domain. AMDF (average magnitude difference function) uses distance between two valley points as the calculated pitch period. However, it has a problem that the algorithm becomes complex in selecting the valley points for the pitch period detection. Therefore, in this paper we proposed the modified AMDF(M-AMDF) algorithm which recognizes the entire minimum valley points as the pitch period of the speech signal by using the rotation transform of AMDF. In addition, a threshold is set to the beginning portion of speech so that it can be used as the selection criteria for the pitch period. Moreover the proposed algorithm is compared with the conventional ones by means of the simulation, and presents better properties than others.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.6
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• pp.628-633
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• 2014

This paper proposes a performance improvement method of the conventional double talk detection method which can operate before convergence of the echo canceller. The proposed method estimates the coefficients of the linear predictive coding(LPC) filter by using the primary input signal. The time-varying threshold for double talk detection is determined based on the LPC filter gain of the primary input signal level. The proposed method can reduce not only false detection rate which means wrong detection of single talk as double talk but also double talk detection delay. Computer simulation was performed using a long-term real speech signals. It is shown that the proposed method improves the conventional method in terms of lowering the false detection rate and shortening the detection delay.

• 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.