• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.58 no.7
• /
• pp.1255-1262
• /
• 2009

The present study explained the phenomenon that low frequency oscillation is synchronized with discrete data obtained from a wide area system, and a sync search method. When a disturbance occurs in an power system, various controllers operate in order to maintain synchronization. If the system's damping is poor, low frequency oscillations continue for a long time and the oscillations are synchronized with one another at specific frequency. The present study estimated dominant modes, magnitude and phase of signals by applying parameter estimation methods to discrete signals obtained from an power system, and performed sync search among wide area signals by comparing the estimated data. Sync search was performed by selecting those with the same frequency and damping constant from dominant oscillation modes included in a large number of signals, and comparing their magnitude and phase. In addition, we defined sync indexes in order to indicate the degree of sync between areas in a wide area system. Furthermore, we proposed a wide area sync search method by normalizing mode magnitude in discrete data obtained from critical generator of the wide area. By applying the sync search method and sync indexes proposed in this study to two area systems, we demonstrated that sync scanning can be performed for discrete signals obtained from power systems.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2000.07a
• /
• pp.585-591
• /
• 2000

The wavelet transform is the most recent technique for processing signals with time-varying spectra. In this paper, the wavelet transform is utilized to improved the assessment and multi-resolution analysis of acoustic emission signals generating in partial discharge. This paper especially deals with the assessment of process statistical parameter using the features extracted from the wavelet coefficients of measured acoustic emission signals in case of applied voltage 20[kv]. Since the parameter assessment using all wavelet coefficients will often turn out leads to inefficient or inaccurate results, we selected that level-3 stage of multi decomposition in discrete wavelet transform. We applied FIR(Finite Impulse Response)digital filter algorithm in discrete to suppression for random noise. The white noise be included high frequency component denoised as decomposition of discrete wavelet transform level-3. We make use of the feature extraction parameter namely, maximum value of acoustic emission signal, average value, dispersion, skewness, kurtosis, etc. The effectiveness of this new method has been verified on ability a diagnosis transformer go through feature extraction in stage of acting(the early period, the last period) .

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.44 no.2
• /
• pp.115-123
• /
• 2021

A lot of sensor and control signals is generated by an industrial controller and related internet-of-things in discrete manufacturing system. The acquired signals are such records indicating whether several process operations have been correctly conducted or not in the system, therefore they are usually composed of binary numbers. For example, once a certain sensor turns on, the corresponding value is changed from 0 to 1, and it means the process is finished the previous operation and ready to conduct next operation. If an actuator starts to move, the corresponding value is changed from 0 to 1 and it indicates the corresponding operation is been conducting. Because traditional fault detection approaches are generally conducted with analog sensor signals and the signals show stationary during normal operation states, it is not simple to identify whether the manufacturing process works properly via conventional fault detection methods. However, digital control signals collected from a programmable logic controller continuously vary during normal process operation in order to show inherent sequence information which indicates the conducting operation tasks. Therefore, in this research, it is proposed to a recurrent neural network-based fault detection approach for considering sequential patterns in normal states of the manufacturing process. Using the constructed long short-term memory based fault detection, it is possible to predict the next control signals and detect faulty states by compared the predicted and real control signals in real-time. We validated and verified the proposed fault detection methods using digital control signals which are collected from a laser marking process, and the method provide good detection performance only using binary values.

• Journal of Korea Society of Industrial Information Systems
• /
• v.6 no.4
• /
• pp.75-80
• /
• 2001

In this paper, we propose a new chaotic synchronization algorithm of using HVPM(Hyperchaotic Volume Preserving Maps) model. The proposed chaotic equation, that is, HVPM model which consists of three dimensional discrete-time simultaneous difference equations and shows uniquely random chaotic attractor using nonlinear maps and modulus function. Pecora and Carrol have recently shown that it is possible to synchronize a chaotic system by sending a signal from the drive chaotic system to the response subsystem. We proposed coupled synchronization algorithm in order to accomplish discrete time hyperchaotic HVPM signals. In the numerical results, two hyperchaotic signals are coupled and driven for accomplishing to the chaotic synchronization systems. And it is demonstrated that HVPM signals have shown the chaotic behavior and chaotic coupled synchronization.

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10a
• /
• pp.239-242
• /
• 1996

A model following control system(MFCS) can give general output signals following desired ones. In previous studies, a method of nonlinear MFCS was proposed by S.Okubo[1]. In this paper, the method of nonlinear MFCS will be extended to discrete time nonlinear systems. It is easy to extend the method to discrete time systems. But in the case .gamma.=1 discrete time systems, the proof becomes difficult, because the transfer function from f(v(k)) to v(k) can't be a positive real function. In this case, to ensure that internal states are stable, a new criterion is proposed.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.21 no.6
• /
• pp.790-798
• /
• 2018

Discrete noise signals from a small scale tactical unmanned aerial vehicle(UAV) propeller were predicted numerically using time domain approach. Two-bladed 29 inch propeller in diameter and 150 kgf in gross weight were used for main parameters of the UAV based on the actual size of the similar scale vehicle. Panel method and Farassat formula A1 were adopted for aerodynamic and aeroacoustic analysis respectively. Time domain signals of both thickness and loading noises were transformed into frequency domain to analyze the discrete noise characteristics. Directivity pattern in a plane perpendicular to the rotating disc plane and attenuation of noise intensity according to double distance were also presented.

• Journal of the Korean Institute of Telematics and Electronics
• /
• v.25 no.12
• /
• pp.1618-1624
• /
• 1988

In this paper, a robust discrete model reference adaptive controller is proposed using a generalized model reference adaptive algorithm for single-input single-output discrete systems. A signal dependent time-varying dead-zone is employed in a generalized adaptive control structure. This adaptive controller is shown to assure the boundedness of the signals of the system even in the presence of bounded external disturbance.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.176-176
• /
• 2000

Generally, discrete-time processing is applied to the uniformly-sampled signals. But, radars emit pulse trains with irregular time instances. In this paper, we formulate the radar pulse train as a stochastic discrete-time dynamic linear model. The estimation task can be done via linear signal processing using Kalman Filter and some considerations. As a result, we can estimate the pulse repetition interval of a pulse train and predict the time instances of the next pulses to be received.

• 제어로봇시스템학회:학술대회논문집
• /
• 1989.10a
• /
• pp.1069-1074
• /
• 1989

A discrete-time learning control for robotic manipulators is studied using its pulse transfer function. Firstly, discrete-time learning stability condition which is applicable to single-input two-outputs systems is derived. Secondly, stability of learning algorithm with position signal is studied. In this case, when sampling period is small, the algorithm is not stable because of an unstable zero of the system. Thirdly, stability of algorithm with position and velocity signals is studied. In this case, we can stabilize the learning control system which is unstable in learning with only position signal. Finally, simulation results on the trajectory control of robotic manipulators using the discrete-time learning control are shown. This simulation results agree well with the analytical ones.

• Journal of the Institute of Convergence Signal Processing
• /
• v.14 no.2
• /
• pp.70-76
• /
• 2013

In this paper, we propose the bandpass form of discrete prolate spheroidal sequences(DPSS) which have the maximal energy concentration in a given passband and as such are very appropriate to obtain a projection of signals. The basic properties of the bandpass DPSS are also presented. Assuming a signal satisfies the finite time support and the essential band-limitedness conditions with a known center frequency, signal representation and interpolation techniques for band-limited signals using the bandpass DPSS are introduced where the reconstructed signal has minimal out-of-band energy. Simulation results are given to present the usefulness of the bandpass DPSS for efficient representation of band-limited signal.