• KIISE Transactions on Computing Practices
• /
• v.21 no.7
• /
• pp.443-451
• /
• 2015

This paper proposes a signal modelling method that can effectively detect the shock wave(SW) sound and muzzle blast(MB) sound from the gunshot of a small arm rifle. In order to localize a counter sniper in battlefield, an accurate detection of both shock wave sound and muzzle blast sound are the necessary keys in estimating the direction and the distance of the counter sniper. To verify the performance of the proposed algorithm, a real gunshot sound in a domestic military shooting range was recorded and analyzed. From the experimental results, the proposed signal modelling method was found to be superior to the comparative system more than 20% in a shock wave detection and 5% in a muzzle blast detection, respectively.

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.36 no.6
• /
• pp.430-436
• /
• 1987

A Detection filter that can be used for the Detection and Isolation of process faults is proposed by the use of fault vector modelling, and is applied to DC Motor fault detection. The proposed detection filter is a new one in a view point that its outputs are the estimates of fault variables(or linear combination of them) while all the existing filters estimate the state of process. By this properties, the process fault detection systems with this filter can be constructed in very simple structure. Besides the simplicity of structure and design procedure, the filter has an useful feature that various types of fault can be estimated via the filter by choosing appropriate fault models.

• 제어로봇시스템학회:학술대회논문집
• /
• 1990.10b
• /
• pp.1228-1233
• /
• 1990

This paper proposes a robust on-line fault detection method for uncertain systems. It is based on the fault detection method [10] accounting for modelling errors, which is shown to have superior performance over traditional methods but has some computational problems so that it is hard to be applied to on-line problems. The proposed method in this paper is an on-line version of the fault detection method suggested in [10]. Thus the method has the same detection performance robust to model uncertainties as that of [10]. Moreover, its computational burden is shown to be considerably lessened so that it is applicable to on-line fault detection problems.

• Structural Monitoring and Maintenance
• /
• v.10 no.1
• /
• pp.63-86
• /
• 2023

Structural health monitoring and damage detection are essential for assessing, maintaining, and rehabilitating structures. Most of the existing damage detection approaches compare the current state structural response with the undamaged vibrational structural response, which is unsuitable for old and existing structures where undamaged vibrational responses are absent. One of the approaches for existing structures, numerical model updating/inverse modelling, available in the literature, is limited to numerical studies with high-end software. In this study, an attempt is made to study the effectiveness of the model updating technique, simplify modelling complexity, and economize its usability. The optimization-based detection problem is addressed by using programmable open-sourced code, OpenSees^® and a derivative-free optimization code, NOMAD^®. Modal analysis is used for damage identification of beam-like structures with several damage scenarios. The performance of the proposed methodology is validated both numerically and experimentally. The proposed method performs satisfactorily in identifying both locations and intensity of damage in structures.

• Journal of Electrical Engineering and Technology
• /
• v.12 no.6
• /
• pp.2219-2226
• /
• 2017

This paper deals with artificial neural network approach for automatic detection of severity level of stator winding fault in induction motor. The problem is faced through modelling and simulation of induction motor with inter coil shorting in stator winding. The sum of the absolute values of difference in the peak values of phase currents from each half cycle has been chosen as the main input to the classifier. Sample values from workspace of Simulink model, which are verified with experiment setup practically, have been imported to neural network architecture. Consideration of a single input extracted from time domain simplifies and advances the fault detection technique. The output of the feed forward back propagation neural network classifies the short circuit fault level of the stator winding.

• 제어로봇시스템학회:학술대회논문집
• /
• 1991.10a
• /
• pp.120-124
• /
• 1991

In this paper, we consider the maneuvering detection and target tracking problem in uncertain linear discrete-time systems. The maneuvering detection is based on X$^{2}$ test[2,71, where Kalman filters have been utilized so far. The target tracking is performed by the maneuvering input compensation based on a maximum likelihood estimator. KF has been known to diverge when some modelling errors exist and fail to detect the maneuvering and to track the target in uncertain systems. Thus this paper adopt the FIR filter[l], which is known to be robust to modelling errors, for maneuvering detection and target tracking problem. Various computer simulations show the superior performance of the FIR filter in this problem.

• Journal of the Korean Institute of Telematics and Electronics
• /
• v.27 no.10
• /
• pp.60-67
• /
• 1990

This paper proposes a model-based fault detection method for linear/nonlinear system having modelling errors, nonlinearities and measurement noise. The system model is represented by the unified operator [5] in order to apply to both the continuous-time and discrete-time problems. The fault detection method suggested here accounts for the effects of noise, model mismatch and nonlinearities. Modelling errors are depicted by additive forms and the nominal model denominator is fixed via prior experiments in order to quantify the nucertainty bound on the parameter estima-tion. The least square method is used to estimate the numerator parameters of the nominal model. performance than traditional methods.

• Journal of electromagnetic engineering and science
• /
• v.16 no.3
• /
• pp.182-190
• /
• 2016

The problem of resolution in antenna ground-penetrating radar (GPR) is very important for the investigation and detection of buried targets. We should solve this problem with software or a numeric method. The purposes of this paper are the modelling and simulation resolution of antenna radar GPR using three antennas, arrays (as in the software REFLEXW), the antenna dipole (as in GprMax2D), and a bow-tie antenna (as in the experimental results). The numeric code has been developed for study resolution antennas by scattered electric fields in mode B-scan. Three frequency antennas (500, 800, and 1,000 MHz) have been used in this work. The simulation results were compared with experimental results obtained by Rial and colleagues under the same conditions.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.61 no.7
• /
• pp.937-943
• /
• 2012

Most of faults occurred in transmission lines are single-phase to ground faults and transient faults. Single-phase auto reclosing is an appropriate scheme to maintain the system stability and restore the system effectively when those faults are occurred. In single-phase auto reclosing scheme, the secondary arc is generated after faulted phase is tripped to eliminate the fault and it is sustained by the capacitive and inductive coupling to the healthy phases. It is important to reclose the faulted phase after fully extinction of secondary arc because of the damage applied to system. Therefore, it is necessary to research on the detection of secondary arc extinction to ensure high success rate of reclosing. In this step, firstly, the accurate modelling of secondary arc should be performed. In this paper, the modelling of secondary arc is performed by using EMTP-RV and the simulation results show that the implemented model is correct and effective.

• Korean Journal of Remote Sensing
• /
• v.35 no.1
• /
• pp.137-149
• /
• 2019

Traffic emissions are the main cause of environmental pollution in cities and respiratory problems amongst people. This study developed a model based on an integration of support vector regression (SVR) algorithm and geographic information system (GIS) to map traffic carbon monoxide (CO) concentrations and produce prediction maps from micro level to macro level at a particular time gap in a day in a very densely populated area (Utara-Selatan Expressway-NKVE, Kuala Lumpur, Malaysia). The proposed model comprised two models: the first model was implemented to estimate traffic CO concentrations using the SVR model, and the second model was applied to create prediction maps at different times a day using the GIS approach. The parameters for analysis were collected from field survey and remote sensing data sources such as very-high-resolution aerial photos and light detection and ranging point clouds. The correlation coefficient was 0.97, the mean absolute error was 1.401 ppm and the root mean square error was 2.45 ppm. The proposed models can be effectively implemented as decision-making tools to find a suitable solution for mitigating traffic jams near tollgates, highways and road networks.