• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1489-1492
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• 1997

To increase reliability and performance of an IMS(Intelligent Manufacturing System), fault tolerant control based on an accurate fault diagnosis is needed. In this paper, robot FDI(fault detection and identification) is proposed for IMS where the robot is controlled with state estimates of a nonlinear filter using a mathematical robot model. The Chi-square distribution is applied fault detection and fault size is estimated by a proposed bias filter. Performance of the proposed algorithm is tested by simulation for studies.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.5
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• pp.666-673
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• 1998

To increase reliability and performance of an IMS(Intelligent Manufacturing System), fault tolerant control based on an accurate fault diagnosis is needed. In this paper, robot FDI(fault detection and identification) is proposed for IMS where the robot is controlled with state estimates of a nonlinear filter using a mathematical robot model. The Chi-square test and GLR(General likelihood ratio) test are applied for fault detection and fault size is estimated by a proposed bias filter. Performance of the proposed algorithm is tested by simulation for studies.

• Journal of Oral Medicine and Pain
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• v.26 no.4
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• pp.309-318
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• 2001

The purposes of the study were (1) to evaluate the olfactory identification ability in those who have bad breath, (2) to determine the olfactory detection threshold for methyl mercaptan in normal subjects and those who have bad breath, and (3) to evaluate the effect of oral hygiene care on the olfactory detection threshold for methyl mercaptan. Sixteen male subjects with bad breath (male odor group), 9 male subjects without bad breath (male non-odor group), and 10 female subjects without bad breath (female non-odor group) were included for the study. Olfactory identification ability was assessed by administrating the Cross-Cultural Smell Identification Test (CC-SIT), and the olfactory detection threshold for methyl mercaptan was measured by two-alternative forced-choice single-staircase detection threshold procedure in a double-blinded condition. The geometric mean of the last four staircase reversal points of a total of seven reversals is used as the threshold. For the male odor group, after 1 month of intensive oral hygiene care for reducing oral volatile sulfur compounds (VSC) concentration, the olfactory detection threshold for methyl mercaptan was measured again and compared to the initial value. The ANOVA was used to test the group difference of olfactory threshold and olfactory identification ability and the paired t-test was used to test the difference of olfactory threshold between before and after reduction of oral VSC in male odor group. The results were as follows : 1. There was no significant difference in olfactory identification ability among those who have bad breath and normal male or female subjects. 2. The olfactory detection threshold for methyl mercaptan was about 8.4 ppb in normal male and female. 3. There was a tendency that male subjects with bad breath showed a higher olfactory detection threshold for methyl mercaptan when compared to those of no bad breath. 4. The olfactory detection threshold for methyl mercaptan returned to a normal level after 1 month of intensive oral hygiene care for reducing oral VSC.

• Structural Engineering and Mechanics
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• v.63 no.6
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• pp.789-797
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• 2017

Recently, some integrated structural identification/damage detection and reliability evaluation of structures with uncertainties have been proposed. However, these techniques are applicable for off-line synthesis of structural identification and reliability evaluation. In this paper, based on the recursive formulation of the extended Kalman filter, an on-line integration of structural identification/damage detection and reliability evaluation of stochastic building structures is investigated. Structural limit state is expanded by the Taylor series in terms of uncertain variables to obtain the probability density function (PDF). Both structural component reliability with only one limit state function and system reliability with multi-limit state functions are studied. Then, it is extended to adopt the recent extended Kalman filter with unknown input (EKF-UI) proposed by the authors for on-line integration of structural identification/damage detection and structural reliability evaluation of stochastic building structures subject to unknown excitations. Numerical examples are used to demonstrate the proposed method. The evaluated results of structural component reliability and structural system reliability are compared with those by the Monte Carlo simulation to validate the performances of the proposed method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.3
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• pp.376-382
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• 2014

E-Actuator is an essential part of an eVGT, it receives the command from the main ECU and controls the vane. An e-Actuator failure can cause an abrupt change in engine output and it may induce an accident. Therefore, it is required to detect anomalies in the e-Actuator in real time to prevent accidents. In this paper, an e-Actuator fault detection method using on-line parameter identification is proposed. To implement on-line fault detection algorithm, many constraints are considered. The test input and sampling rate are selected considering the constraints. And new recursive system identification algorithm is proposed which reduces the memory and MCU power dramatically. The relationship between the identified parameters and real elements such as gears, spring and motor are derived. The fault detection method using the relationship is proposed. The experiments with the real broken gears show the effectiveness of the proposed algorithm. It is expected that the real time fault detection is possible and it can improve the safety of eVGT system.

• Journal of the Korean Operations Research and Management Science Society
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• v.40 no.2
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• pp.31-42
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• 2015

The purpose of airborne radars is to detect and identify approaching targets as early as possible. If the targets are identified as enemies, detection systems must provide defense systems with information of the targets to counter. Though many previous studies based on the detection theory of the target have shown various ways to derive detection probability of each radar, optimal arrangement of radars for effective detection, and determination of the search pattern, they did not reflect the fact that most military radar sites run multiple radars in order to increase the accuracy of identifications by radars. In this paper, we propose a model to analyze the probability of identification generated by the multiple radars using non-homogeneous absorbing markov chains. Our results are expected to help the military commanders counter the enemy targets effectively by using radars in a way to maximize the identification rate of targets.

• Smart Structures and Systems
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• v.15 no.1
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• pp.57-80
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• 2015

Extended Kalman Filter (EKF) has been widely used for structural identification and damage detection. However, conventional EKF approaches require that external excitations are measured. Also, in the conventional EKF, unknown structural parameters are included as an augmented vector in forming the extended state vector. Hence the sizes of extended state vector and state equation are quite large, which suffers from not only large computational effort but also convergence problem for the identification of a large number of unknown parameters. Moreover, such approaches are not suitable for intelligent structural damage detection due to the limited computational power and storage capacities of smart sensors. In this paper, a two-stage and two-step algorithm is proposed for the identification of structural damage as well as unknown external excitations. In stage-one, structural state vector and unknown structural parameters are recursively estimated in a two-step Kalman estimator approach. Then, the unknown external excitations are estimated sequentially by least-squares estimation in stage-two. Therefore, the number of unknown variables to be estimated in each step is reduced and the identification of structural system and unknown excitation are conducted sequentially, which simplify the identification problem and reduces computational efforts significantly. Both numerical simulation examples and lab experimental tests are used to validate the proposed algorithm for the identification of structural damage as well as unknown excitations for structural health monitoring.

• ETRI Journal
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• v.46 no.4
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• pp.697-707
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• 2024

Music identification is widely regarded as a solved problem for music searching in quiet environments, but its performance tends to degrade in TV broadcast audio owing to the presence of dialogue or sound effects. In addition, constructing an accurate dataset for measuring the performance of background music monitoring in TV broadcast audio is challenging. We propose a framework for monitoring background music by automatic identification and introduce a background music cue sheet. The framework comprises three main components: music identification, music-speech separation, and music detection. In addition, we introduce the Cue-K-Drama dataset, which includes reference songs, audio tracks from 60 episodes of five Korean TV drama series, and corresponding cue sheets that provide the start and end timestamps of background music. Experimental results on the constructed and existing datasets demonstrate that the proposed framework, which incorporates music identification with music-speech separation and music detection, effectively enhances TV broadcast audio monitoring.

• 전기의세계
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• v.28 no.3
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• pp.57-64
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• 1979

The static state estimation are divided into two groups; estimation and detection & identification. This paper centers on detection and identification algorithm. Especially, the identification of line errors is focused on and is performed by the extended W.L.S. algorithm with line swithching states. Here, line switching states mean the discrete values of line admittance which are influenced by unexpected line switching. The numerical results are obtained from the assumption that the noise vector is independent zero mean Gaussian random variables.

• The Journal of the Acoustical Society of Korea
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• v.25 no.8
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• pp.375-382
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• 2006

This paper is about how to identify emotional information and how to detect a specific emotion from speech signals. For emotion identification and detection task. we use long-term acoustic feature parameters and select the optimal Parameters using the feature selection technique based on F-score. We transform the conventional SVM into probabilistic output SVM for our emotion identification and detection system. In this paper we propose three approximation methods for log-likelihoods in a hypothesis test and compare the performance of those three methods. Experimental results using the SUSAS database showed the effectiveness of both feature selection and Probabilistic output SVM in the emotion identification task. The proposed methods could detect anger emotion with 91.3% correctness.