• Journal of Institute of Control, Robotics and Systems
• /
• v.5 no.1
• /
• pp.16-24
• /
• 1999

본 논문은 불확정 시스템의 견실한 이상검출기법의 적용을 위한 실시간 이상검출기법에 대하여 다루며 대상 시스템은 산업용보일러 시스템이다. 본 논문에서 기술된 이상검출기법은 Kwon (1994) 등에 의하여 이미 제시된바 있는 견실한 이상검출기법의 오프라인 배치 처리 알고리즘을 실시간 적용을 위해 확장된 것이며 모델링 오차에 의한 불확실성, 비선형 시스템을 특정 동작점에서 선형화 하는 과정에서 발생하는 선형화 오차, 잡음 등을 고려하였고, 보일러 시스템을 대상으로 한 모의 실험을 통해 본 알고리즘의 우수성을 보였다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2019.11a
• /
• pp.169-171
• /
• 2019

This paper proposes an algorithm to detect persons in bitstream domain by skipping a reconstruction picture process in HEVC decoding. A new 3-channel feature extraction map is introduced in this paper by modelling the relationship between bits per CU density, average PU shape in CU, and total transform coefficients in CU from syntax elements. A state-of-the-art of YOLOv3 detection algorithm is used to detect and localize person on extracted feature maps. Based on the experimental results, the proposed person detection framework can achieve mAP of 0.68 and be able to find persons on feature maps. In addition, the proposed person detection can save decoding time about 60% by removing reconstruction picture process.

• Proceedings of the KSRS Conference
• /
• 2005.10a
• /
• pp.301-304
• /
• 2005

For detecting an intrusion based on the anomaly of a user's activities, previous works are concentrated on statistical techniques or frequent episode mining in order to analyze an audit data. But, since they mainly analyze the average behaviour of user's activities, some anomalies can be detected inaccurately. Therefore, we propose an anomaly detection method that utilizes an associative classification for modelling intrusion detection. Finally, we proof that a prediction model built from associative classification method yields better accuracy than a prediction model built from a traditional methods by experimental results.

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.39 no.7
• /
• pp.729-739
• /
• 1990

This paper deals with the fault detection problem in uncertain linear/non-linear systems having both undermodelling and noise. A robust fault detection method is presented which accounts for the effects of noise, model mismatch and nonlinearities. The basic idea is to embed the unmodelled dynamics in a stochastic process and to use the nominal model with a predetermined fixed denominator. This allows the input /output relationship to be represented as a linear function of the system parameters and also facilitate the quatification of the effect of noise, model mismatch and linearization errors on parameter estimation by the Bayesian method. Comparisons are made via simulations with traditional fault detection methods which do not account for model mismatch or linearization errors. The new method suggested in this paper is shown to have a marked improvement over traditional methods on a number of simulations, which is a consequence of the fact that the new method explicitly for the effects of undermodelling and linearization errors.

• International Journal of Computer Science & Network Security
• /
• v.22 no.5
• /
• pp.97-102
• /
• 2022

Network intrusion detection is becoming an increasing necessity for both organizations and individuals alike. Detecting intrusions is one of the major components that aims to prevent information compromise. Automated systems have been put to use due to the voluminous nature of the domain. The major challenge for automated models is the noise and data imbalance components contained in the network transactions. This work proposes an ensemble model, Attribute Subset Selector Bagging (ASUB) that can be used to effectively handle noise and data imbalance. The proposed model performs attribute subset based bag creation, leading to reduction of the influence of the noise factor. The constructed bagging model is heterogeneous in nature, hence leading to effective imbalance handling. Experiments were conducted on the standard intrusion detection datasets KDD CUP 99, Koyoto 2006 and NSL KDD. Results show effective performances, showing the high performance of the model.

• Smart Structures and Systems
• /
• v.18 no.3
• /
• pp.569-584
• /
• 2016

This paper presents a method for stochastic modelling of fatigue crack growth and optimising inspection and maintenance strategy for the structural members of steel bridges. The fatigue crack evolution is considered as a stochastic process with uncertainties, and the Gamma process is adopted to simulate the propagation of fatigue crack in steel bridge members. From the stochastic modelling for fatigue crack growth, the probability of failure caused by fatigue is predicted over the service life of steel bridge members. The remaining fatigue life of steel bridge members is determined by comparing the fatigue crack length with its predetermined threshold. Furthermore, the probability of detection is adopted to consider the uncertainties in detecting fatigue crack by using existing damage detection techniques. A multi-objective optimisation problem is proposed and solved by a genetic algorithm to determine the optimised inspection and maintenance strategy for the fatigue affected steel bridge members. The optimised strategy is achieved by minimizing the life-cycle cost, including the inspection, maintenance and failure costs, and maximizing the service life after necessary intervention. The number of intervention during the service life is also taken into account to investigate the relationship between the service life and the cost for maintenance. The results from numerical examples show that the proposed method can provide a useful approach for cost-effective inspection and maintenance strategy for fatigue affected steel bridges.

• Structural Engineering and Mechanics
• /
• v.43 no.3
• /
• pp.371-394
• /
• 2012

Accurate finite element (FE) models are needed in many applications of Civil Engineering such as health monitoring, damage detection, structural control, structural evaluation and assessment. Model accuracy depends on both the model structure (the form of the equations) and the model parameters (the coefficients of the equations), and can be generally improved through that process of experimental reconciliation known as model updating. However, modelling errors, including (i) errors in the model structure and (ii) errors in parameters excluded from adjustment, may bias the solution, leading to an updated model which replicates measurements but lacks physical meaning. In this paper, an application of ambient-vibration-based model updating to a large-scale benchmark prototype of a building structure is reported in which both types of error are met. The error in the model structure, originating from unmodelled secondary structural elements unexpectedly working as resonant appendages, is faced through a reduction of the experimental modal model. The error in the model parameters, due to the inevitable constraints imposed on parameters to avoid ill-conditioning and under-determinacy, is faced through a multi-model parameterization approach consisting in the generation and solution of a multitude of models, each characterized by a different set of updating parameters. Results show that modelling errors may significantly impair updating even in the case of seemingly simple systems and that multi-model reasoning, supported by physical insight, may effectively improve the accuracy and robustness of calibration.

• Communications for Statistical Applications and Methods
• /
• v.23 no.6
• /
• pp.497-515
• /
• 2016

Modern processes often monitor more than one quality characteristic that are referred to as multivariate statistical process control (MSPC) procedures. The MSPC is the most rapidly developing sector of statistical process control and increases interest in the simultaneous inspection of several related quality characteristics. Most multivariate detection procedures based on a multi-normality assumptions are independent, but there are many processes that assume non-normality and correlation. Many multivariate control charts have a lack of related joint distribution. Copulas are tool to construct multivariate modelling and formalizing the dependence structure between random variables and applied in several fields. From copula literature review, there are a few copula to apply in MSPC that have multivariate control charts, and represent a successful tool to identify an out-of-control process. This paper presents various types of copulas modelling for the multivariate control chart. The performance measures of the control chart are the average run length (ARL) and the average number of observations to signal (ANOS). Furthermore, a Monte Carlo simulation is shown when the observations were from an exponential distribution.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1995.10a
• /
• pp.1089-1092
• /
• 1995

A planar vibratory gyroscope using electrostatic actuation and electromagnetic detection is proposed. The gyroscope has large sensitivity and can be fabricated by using surface micrimachining, bulk micromachining and conventional machining technology. In this paper, the gyroscope and the electromagnetic detecting system equations are derived to determine the output characteristics for the planar vibratory gyroscope using electrostatic acturation and electromagnetic detection. The maximum output is obtained when the driving frequencyequals to the detecting frequency. The resonant frequencies of the resonator are determined by the beam stiffness, i.e. the material constants and spring dimensions. The dimensions of the beams are determined using the analytic vibration modelling. The expected resonant frequencies are 200Hz both and the sensitivity is 62mV/deg/sec with 4000 electronic circuit amplifying coefficient for an AC drive voltage of 3V bias voltage of 15V and DC field current of 50 mA.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.64 no.11
• /
• pp.1605-1610
• /
• 2015

This study describes a new method to analyze phonocardiogram acquired from electronic stethoscope. The method uses the formant frequencies of linear prediction spectrum of the phonocardiogram and proposes a novel method for formant detection using the smoothing and the first and second derivatives. For this, stethoscope sounds are acquired in university hospital. The stethoscope signals are preprocessed and analyzed by the Burg algorithm, a kind of linear prediction analysis. Based on the linear prediction spectra, the formant frequencies are estimated. The proposed method has shown better performance in formant frequency detection than the conventional peak picking method.