• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.6
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• pp.241-251
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• 2002

The effect of sensor faults in direct torque control(DTC) based induction motor drives is analyzed and a new Instrument fault detection isolation scheme(IFDIS) is proposed. The proposed IFDIS, which operated in real-time, detects and isolates the incipient fault(s) of speed sensor and current sensors that provide the feedback information. The scheme consists of an adaptive gain scheduling observer as a residual generator and a special sequential test logic unit. The observer provides not only the estimate of stator flux, a key variable in DTC system, but also the estimates of stator current and rotor speed that are useful for fault detection. With the test logic, the IFDIS has the functionality of fault isolation that only multiple estimator based IFDIS schemes can have. Simulation results for various type of sensor faults show the detection and isolation performance of the IFDIS and the applicability of this scheme to fault tolerant control system design.

• International Journal of Control, Automation, and Systems
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• v.4 no.3
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• pp.333-343
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• 2006

In this paper, the baseline-length information is directly modeled as a measurement for the Wald test, which speeds up the resolution convergence of the integer ambiguity of GPS carrier phase measurements. The convergent speed improvement is demonstrated using numerical simulation and real experiments. It is also shown that the integer ambiguities can be resolved using only four actual satellite measurements with very reasonable convergence speed, if the baseline-length information is used just like one additional observable satellite measurement. Finally, it is shown that the improvement of convergence speed of the Wald test is due to the increase of the probability ratio with the use of the baseline-length constraint.

• ETRI Journal
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• v.31 no.2
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• pp.193-200
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• 2009

In this paper, we study the performance of subspace-based multiuser detection techniques for multicarrier code-division multiple access (MC-CDMA) systems. We propose an improvement in the PASTd algorithm by cascading it with the classical Gram-Schmidt procedure to orthonormalize the eigenvectors after their sequential extraction. The tracking of signal subspace using this algorithm, which we call OPASTd, has a faster convergence as the eigenvectors are orthonormalized at each discrete time sample. This improved PASTd algorithm is then used to implement the subspace blind adaptive multiuser detection for MC-CDMA. We also show that, for multiuser detection, the complexity of the proposed scheme is lower than that of many other orthogonalization schemes found in the literature. Extensive simulation results are presented and discussed to demonstrate the performance of the proposed scheme.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.10
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• pp.880-887
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• 2005

This paper presents a fault diagnosis(detection and isolation) approach for the Ammunition Transport Automation system(ATAS). Due to limited time and information available during its cyclic operation, the on-line fault detection algorithm consists of sequential test logics referring to the normal states, which can be considered as a kind of expert system. If a failure were detected, the off-line isolation algorithm finds the fault location through trained ART2 neural network. By the results of simulations and some on-line field test, it has been shown that the presented approach is effective enough and applicable to related automation systems.

• Proceedings of the KIEE Conference
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• 2003.11a
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• pp.100-102
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• 2003

This paper describes a time-sequential simulation technique for the reliability evaluation of a distribution system including Photovoltaics(PV) Generation. A three-state model of a PV is presented, considering variable radiation and the forced outage rate. A test distribution system is utilized to illustrate the proposed model. The effects on the distribution system reliability of the PV parameters are examined and illustrated.

• Journal of information and communication convergence engineering
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• v.20 no.2
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• pp.79-89
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• 2022

To compute the mean and variance of component-based reliability software, we focused on path-based reliability analysis. System reliability depends on the transition probabilities of components within a system and reliability of the individual components as basic input parameters. The uncertainty in these parameters is estimated from the test data of the corresponding components and arises from the software architecture, failure behaviors, software growth models etc. Typically, researchers perform Monte Carlo simulations to study uncertainty. Thus, we considered a Markov chain Monte Carlo (MCMC) simulation to calculate uncertainty, as it generates random samples through sequential methods. The MCMC approach determines the input parameters from the probability distribution, and then calculates the average approximate expectations for a reliability estimation. The comparison of different techniques for uncertainty analysis helps in selecting the most suitable technique based on data requirements and reliability measures related to the number of components.

• The Korean Journal of Applied Statistics
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• v.36 no.4
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• pp.279-294
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• 2023

It is well known to generate synthetic data sets by the sequential regression multiple imputation (SRMI) method. The R-package synthpop are widely used for generating synthetic data by the SRMI approaches. In this paper, I suggest generating synthetic data based on the probabilistic principal component analysis (PPCA) method. Two simple data sets are used for a simulation study to compare the SRMI and PPCA approaches. Simulation results demonstrate that pairwise coefficients in synthetic data sets by PPCA can be closer to original ones than by SRMI. Furthermore, for the various data types that PPCA applications are well established, such as time series data, the PPCA approach can be extended to generate synthetic data sets.

• Asian-Australasian Journal of Animal Sciences
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• v.19 no.8
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• pp.1164-1173
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• 2006

Cellulase from Aspergillus niger, (${\alpha}$-amylase from Bacillus sp. and protease from Bacillus globigii were used as enzyme sources in this study to examine how their respective substrates protect them in two kinds of simulated gastrointestinal tract digesting processes. Avian total digest tract simulation test showed that filter paper, Avicel and cellulose resulted in 7.7, 6.4 and 7.4 times more activity than of unprotected cellulose, respectively. Protease with addition of casein, gelatin or soybean protein showed no significant protection response. Starch protected amylase to be 2.5 times activity of the unprotected one. Monogastric animal total tract digestion simulation test showed that filter paper, Avicel and cellulose resulted in 5.9, 9.0 and 8.8 times activity of unprotected cellulase, respectively. Casein, gelatin and soybean protein resulted in 1.2, 1.3 and 2.0 times activity of unprotected protease, respectively. Starch did not protect amylase activity in monogastric animal total tract simulation. Protection of mixed enzymes by substrates in two animal total tract simulation tests showed that filter paper in combination with soybean protein resulted in 1.5 times activity of unprotected cellulose, but all substrates tested showed no significant protection effect to protease. Soybean protein and starch added at the same time protected the amylase activity to be two times of the unprotected one. Test of non-purified substrate protection in two animal total digest tract simulation showed that cellulase activity increased as BSA (bovine serum albumin) concentration increased, with the highest activity to be 1.3 times of unprotected enzyme. However, BSA showed no significant protection effect to protease. Amylase activity increased to 1.5 times as BSA added more than 1.5% (w/v). Cellulase activity increased to 1.5 times as soybean hull was added higher than 1.5%. Amylase had a significant protection response only when soybean hull added up to 2%. Protease activity was not protected by soybean hull to any significant extent.

• Nuclear Engineering and Technology
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• v.53 no.1
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• pp.148-163
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• 2021

Timely fault identification is important for safe and reliable operation of the electric valve system. Many research works have utilized different data-driven approach for fault diagnosis in complex systems. However, they do not consider specific characteristics of critical control components such as electric valves. This work presents an integrated shallow-deep fault diagnostic model, developed based on signals extracted from DN50 electric valve. First, the local optimal issue of particle swarm optimization algorithm is solved by optimizing the weight search capability, the particle speed, and position update strategy. Then, to develop a shallow diagnostic model, the modified particle swarm algorithm is combined with support vector machine to form a hybrid improved particle swarm-support vector machine (IPs-SVM). To decouple the influence of the background noise, the wavelet packet transform method is used to reconstruct the vibration signal. Thereafter, the IPs-SVM is used to classify phase imbalance and damaged valve faults, and the performance was evaluated against other models developed using the conventional SVM and particle swarm optimized SVM. Secondly, three different deep belief network (DBN) models are developed, using different acoustic signal structures: raw signal, wavelet transformed signal and time-series (sequential) signal. The models are developed to estimate internal leakage sizes in the electric valve. The predictive performance of the DBN and the evaluation results of the proposed IPs-SVM are also presented in this paper.

• Fire Science and Engineering
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• v.30 no.1
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• pp.31-36
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• 2016

This study investigates the characteristics of thermal decomposition of an epoxy-based intumescent paint using thermogravimetric analysis (TGA) and numerical simulation. A mathematical and numerical model is introduced to describe mass loss profiles of the epoxy-based intumescent coating induced by the thermal decomposition process. The decomposition scheme covers a range of complexity by employing simplified 4-step sequential reactions to describe the simultaneous thermal decomposition processes. The reaction rates are expressed by the Arrhenius law, and reaction parameters are optimized to fit the degradation behavior seen during thermogravimetric (TG) experiments. The experimental results show a major 2-step degradation under nitrogen and a 3-step degradation in an air environment. The experiment also shows that oxygen takes part in the stabilization of the intumescent coating between 200 and $500^{\circ}C$. The simulation results show that the proposed model effectively predicts the experimental mass loss as a function of time except for temperatures above $800^{\circ}C$, which were intentionally not included in the model. The maximum error in the simulation was less than 3%.