• Journal of Information Processing Systems
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• v.15 no.1
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• pp.159-168
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• 2019

Accurate forest fires detection algorithms remain a challenging issue, because, some of the objects have the same features with fire, which may result in high false alarms rate. This paper presents a new video-based, image processing forest fires detection method, which consists of four stages. First, a background-subtraction algorithm is applied to detect moving regions. Secondly, candidate fire regions are determined using CIE $L{\ast}a{\ast}b{\ast}$ color space. Thirdly, special wavelet analysis is used to differentiate between actual fire and fire-like objects, because candidate regions may contain moving fire-like objects. Finally, support vector machine is used to classify the region of interest to either real fire or non-fire. The final experimental results verify that the proposed method effectively identifies the forest fires.

• The Journal of the Acoustical Society of Korea
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• v.41 no.2
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• pp.199-209
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• 2022

Typical anomaly detection algorithms are trained by using prior data. Thus the batch learning based algorithms cause inevitable performance degradation when characteristics of newly incoming normal data change over time. We propose an online anomaly detection algorithm which can consider the gradual characteristic changes of incoming normal data. The proposed algorithm based on one-class classification model includes both offline and online learning procedures. In offline learning procedure, the algorithm learns the prior data to be close to centroid of the latent space and then updates the centroid of the latent space incrementally by new incoming data. In the online learning, the algorithm continues learning by using the updated centroid. Through experiments using public underwater acoustic data, the proposed online anomaly detection algorithm takes only approximately 2 % additional learning time for the incremental centroid update and learning. Nevertheless, the proposed algorithm shows 19.10 % improvement in Area Under the receiver operating characteristic Curve (AUC) performance compared to the offline learning model when new incoming normal data comes.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2006.04a
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• pp.240-247
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• 2006

Piezoelectric sensor-based health monitoring technique using a two-step support vector machine (SYM) classifier is discussed for damage identification of a railroad track. An active sensing system composed of two PZT patches was investigated in conjunction with both impedance and guided wave propagation methods to detect two kinds of damage of the railroad track (one is a hole damage of 0.5cm in diameter at web section and the other is a transverse cut damage of 7.5cm in length and 0.5cm in depth at head section). Two damage-sensitive features were extracted one by one from each method; a) feature I: root mean square deviations (RMSD) of impedance signatures and b) feature II: wavelet coefficients for $A_0$ mode of guided waves. By defining damage indices from those damage-sensitive features, a two-dimensional damage feature (2-D DF) space was made. In order to minimize a false-positive indication of the current active sensing system, a two-step SYM classifier was applied to the 2-D DF space. As a result, optimal separable hyper-planes were successfully established by the two-step SYM classifier: Damage detection was accomplished by the first step-SYM, and damage classification was also carried out by the second step-SYM. Finally, the applicability of the proposed two-step SYM classifier has been verified by thirty test patterns.

• Proceedings of the KIPE Conference
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• 1998.10a
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• pp.615-619
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• 1998

Active power filter is a kind of device used for compensating instantaneous reactive and harmonic current in three-phase circuits. An essential technology that determines the behavior of an active power filter is the method of detecting the distorted current. Using three-dimensional space vectors, this paper describes a simple method for detecting the distorted current without any coordinate transformation. The effectiveness of the novel method is verified by the theoretical analysis and simulation.

• Journal of Electrical Engineering and information Science
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• v.2 no.4
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• pp.86-91
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• 1997

A method of detecting the three phase currents for a voltage-fed pusle width modulated(PWM) inverter is proposed, where only one current sensor is utilized on the dc-link. The proposed method has the constant sampling time by employing he modified space-vector PWM technique which generates the rearranged switching pattern to detect a phase current from ad dc-link current. Experimental results show that eh proposed scheme provides a very good detection method of three phase currents without phase current sensors. This method is very simple and has small detection errors.

• Proceedings of the KIEE Conference
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• 1996.07a
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• pp.333-335
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• 1996

A method for detecting the three-phase currents of a voltage-fed pulsewidth modulated(PWM) inverter is proposed by utilizing only one current sensor placed on the dc-link. The proposed space vector PWM technique is two phase modulated PWM, this enables to detect the phase currents from only one DC link current sensor. The proposed method is simple, reduces the cost, and provides the small detection errors.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.4
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• pp.477-481
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• 2010

In this paper, we try to classify behavior patterns of a person around a bed based on a wireless sensor network system. We define five behavioral patterns and three states of a person around a bed which is described by a state machine. We collected data sensed by motion detection and vibration sensors installed around a bed from which a feature vector was extracted. Based on feature vector corresponding to behavioral patterns and the state machine, we established a model for behavioral patterns. To validate the model, experiments on subjects were performed and the model was fixed. These experimental results revealed that behavior patterns of a person around a bed can be classified well.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1463-1467
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• 2005

This paper proposes an analytical redundancy technique for fault diagnostics of the sensor in steer-by-wire system. We use incorporating vehicle dynamics modeling into the design of a diagnostic system for steer-by-wire system. The use of a model of vehicle dynamics improves the speed and accuracy of the diagnoses. The proposed fault diagnostics algorithm is based on parity-space methods to generate residuals. To reduce the effects of modeling uncertainty and dynamic transients, the residuals are subject to filtering. We construct diagnostic system consisting residual threshold for detection and isolator with using the directional residual vector.

• Proceedings of the KIEE Conference
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• 1998.07f
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• pp.1993-1995
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• 1998

The compensation problem of error and various problems due to delay of speed sensor itself and speed detection have issued in case using speed sensor in the adjustable speed control of induction motor. This paper have applied the stator flux oriented vector control algorithm and space voltage vector PWM method in order to improve an dynamic character of voltage-source inverter system, and also used the better IP controller in the speed response than Pl controller as speed controller. This paper estimated the rotator speed using input current of inductor motor and flux component invoked through voltage drop by terminal voltage and stator resistor.

• Smart Structures and Systems
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• v.6 no.2
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• pp.115-133
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• 2010

A modal filter is a tool used to extract the modal coordinates of each individual mode from a system's output. This is achieved by mapping the response vector from the physical space to the modal space. It decomposes the system's responses into modal coordinates, and thus, on the output of the filter, the frequency response with only one peak corresponding to the natural frequency to which the filter was tuned can be obtained. As was shown in the paper (Deraemecker and Preumont 2006), structural modification (e.g. a drop in stiffness or mass due to damage) causes the appearance of spurious peaks on the output of the modal filter. A modal filter is, therefore, a great indicator of damage detection, with such advantages as low computational effort due to data reduction, ease of automation and lack of sensitivity to environmental changes. This paper presents the application of modal filters for the detection of stiffness changes. Two experiments were conducted: the first one using the simulation data obtained from the numerical 7DOF model, and the second one on the experimental data from a laboratory stand in 4 states of damage.