• Journal of Institute of Control, Robotics and Systems
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• v.21 no.3
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• pp.283-289
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• 2015

In this paper, a GNSS interference detection algorithm based on an adaptive fading Kalman filter is proposed to detect a spoofing signal which is one of the threatening GNSS intentional interferences. To detect and mitigate the spoofing signal, the fading factor of the filter is used as a detection parameter. For simulation, the effect of the spoofing signal is modeled by the ramp type bias error of the pseudorange to emulate a smart spoofer and the change of the fading factor value according to ramp type bias error is quantitatively analyzed. In addition, the detection threshold is established to detect the spoofing signal by analyzing the change of the error covariance and the effect of spoofing is mitigated by controlling the Kalman gain of the filter. To verify the performance analysis of the proposed algorithm, various simulations are implemented. Through the results of simulations, we confirmed that the proposed algorithm works well.

• Proceedings of the SAREK Conference
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• 2008.11a
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• pp.452-457
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• 2008

We performed a study on the contaminant transfer in a clean space for the location of product and fan filter unit using computational fluid dynamics analysis. To simplify the real product moving process, three different non-moving cases regrading the locations of product were selected: no product, at the lower side, and at the upper and lower sides. And to investigate the characteristics of the contaminant transfer, the arrangement of fan filter units was varied. Local mean air-age and contaminant distribution were used as evaluation indices. From the results, the contaminant transfer to the product was the most when the products were simultaneously located at the upper and lower sides. And the contaminant was easily exhausted regardless of the location of product when the fan filter units were properly arranged at the top side of the clean space.

• International Journal of Automotive Technology
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• v.8 no.2
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• pp.165-177
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• 2007

Predictions of diesel particulate filtration are typically made by modeling of a particle collection, and providing particle trapping levels in terms of a pressure drop. In the present study, a series of single channel diesel particulate filter (DPF) experiments are conducted, the pressure traces are inversely analyzed and essential filtration parameters are deducted for model closure. A DPF filtration model is formulated with a non-linear description of soot cake regression. Dependence of soot cake porosity, packing density, permeability, and soot density in filter walls on convective-diffusive particle transportation is examined. Sensitivity analysis was conducted on model parameters, relevant to the mode of transition. Soot cake porosity and soot packing density show low degrees of dispersion with respect to the Peclet number and have asymptotes at 0.97 and $70\;kg/m^3$, respectively, at high Peclet number. Soot density in the filter wall, which is inversely proportional to filter wall Peclet number, controls the filtration mode transition but exerts no influence on termination pressure drop. The percolation constant greatly alters the extent of pressure drop, but is insensitive to volumetric flow rate or temperature of exhaust gas at fixed operation mode.

• Journal of the Korean Society of Visualization
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• v.17 no.2
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• pp.48-57
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• 2019

A straightforward strategy for particle image velocimetry (PIV) interrogation and post-processing has been proposed, aiming at reducing errors and clarifying vortex structures. The interrogation window size should be kept small to reduce bias error and improve spatial resolution. A spatial filter is then applied to the velocity field to reduce random error and clarify flow structure. The performance of three popular spatial filters were assessed: box filter, median filter, and local quadratic polynomial regression filter. In order to quantify random uncertainty, the image matching (IM) method is applied to an experimental dataset of homogeneous and isotropic turbulence (HIT) obtained by 2D-PIV. We statistically analyze the uncertainty propagation through the spatial filters, and verify the reduction in random uncertainty. Moreover, we illustrate that the spatial filters help clarify vortex structures using vortex identification criteria. As a result, PIV random uncertainty was reduced and the vortex structures became clearer by spatial filtering.

• Smart Structures and Systems
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• v.21 no.6
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• pp.741-749
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• 2018

Structural health monitoring (SHM) systems are necessary to achieve smart predictive maintenance and repair planning as well as they lead to a safe operation of mechanical structures. In the context of vibration-based SHM the measured structural responses are employed to draw conclusions about the structural integrity. This usually leads to a mathematically illposed inverse problem which needs regularization. The restriction of the solution set of this inverse problem by using prior information about the damage properties is advisable to obtain meaningful solutions. Compared to the undamaged state typically only a few local stiffness changes occur while the other areas remain unchanged. This change can be described by a sparse damage parameter vector. Such a sparse vector can be identified by employing $L_1$-regularization techniques. This paper presents a novel framework for damage parameter identification by combining sparse solution techniques with an Extended Kalman Filter. In order to ensure sparsity of the damage parameter vector the measurement equation is expanded by an additional nonlinear $L_1$-minimizing observation. This fictive measurement equation accomplishes stability of the Extended Kalman Filter and leads to a sparse estimation. For verification, a proof-of-concept example on a quadratic aluminum plate is presented.

• Journal of the Korean Society for Precision Engineering
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• v.31 no.10
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• pp.905-911
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• 2014

A Loss-in-Weight (LIW) feeder, a type of automated measuring device, is a continuous feeder used in many mass production industries. Due to its versatility, there have been constant demands of LIW feeders in food production supply lines as well as chemical and pharmaceutical industries. In this paper, the process of designing a LIW feeder system with better performance will be examined and compared with commercial products. This system is characterized by low pass Butterworth filter and feed forward PI control. The filter is for noise disposal caused by dynamic condition of a LIW feeder. The feed forward PI control, based on linearity feature of feeders, is adequate for stable driving of the system. At the end, a possible evaluation method of LIW system will be proposed to verify the specific achievement of this paper.

• International Journal of Control, Automation, and Systems
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• v.6 no.3
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• pp.424-433
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• 2008

An ownship with towed array sonar (TAS) has limited maneuvers due to its dynamic feature, bearing and frequency measurements of a target which are not detected continuously but are often lost in ocean environment. We propose a pre-processing algorithm for the faded bearing and frequency measurements to solve the BFTMA problem of TAS under limited detection conditions. The proposed pre-processing algorithm to restore the faded bearing and frequency measurements is implemented to perform a BFTMA filter even if the measurements of a target are not continuously detected. The Modified Gain Extended Kalman Filter (MGEKF) method based on the Interacting Multiple Model (IMM) structure is applied for a BFTMA filter algorithm to estimate the target. Simulations for the various conditions were carried out to verify the applicability of the proposed algorithms, and confirmed superior estimation performance compared with the existing Bearings-Only TMA (BOTMA).

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.2
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• pp.98-103
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• 2013

Conventional method to estimate mass of particulate matter accumulated in diesel particulate filter is to use pressure difference between upstream and downstream of the filter. Then measured pressure difference should be compared that of clean condition which is no particulate matter accumulated in DPF. During regeneration soot oxidation is also estimated by same method. This methodology, however, has demerit on accuracy because of pressure difference deviation of clean DPFs and pressure difference caused by non-carbon based PM which is different from that of caused by carbon based PM. This study suggests new methodology to estimate accumulated soot oxidation rate through exhaust gas characteristics during regeneration. Results, more high accuracy of soot oxidation was obtained by analysis of relationship between fuel mass and concentration of carbon dioxide and oxygen.

• Clean Technology
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• v.8 no.3
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• pp.141-149
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• 2002

In the present study, flow fields inside a photocatalysis air cleaning system have been investigated to obtain the data for optimum design of the system. For this investigation, we first predicted the pressure drop of a photocatalysis filter. Based on this calculation, we replaced the filter by the porous media and analysed the performance of the entire system. This porous media assumption could predict characteristics of the cylindrical photocatalysis filter within 1%. Two cases of the cylindrical filter were considered in the present calculations. The first case had an inside diameter of 6 mm, while the second case had an inside diameter of 20 mm. It was found that the first case of filter showed a pressure drop three times higher than that of the second case. In addition, the cylindrical filter equipped with a housing to hold a number of cylinders in a bundle was also analyzed. When the housing was present, the pressure drop increased. It was found that the pressure drop is 8 times higher than that of the case when the housing was not equipped.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.329-334
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• 1998

This work is concerned with the problem of tracking a maneuvering target. In this paper, an error monitoring and recovery method of perception net is utilized to improve tracking performance for a highly maneuvering tar-get. Many researches have been performed in tracking a maneuvering target. The conventional Interacting Multiple Model (IMM) filter is well known as a suboptimal hybrid filter that has been shown to be one of the most cost-effective hybrid state estimation scheme. The subfilters of IMM can be considered as fusing its initial value with new measurements. This approach is also shown in this paper. Perception net based error monitoring and recovery technique, which is a kind of geometric data fusion, makes it possible to monitor errors and to calibrate possible biases involved in sensed data and extracted features. Both detecting a maneuvering target and compensating the estimated state can be achieved by employing the properly implemented error monitoring and recovery technique. The IMM filter which employing the error monitoring and recovery technique shows good tracking performance for a highly maneuvering target as well as it reduces maximum values of estimation errors when maneuvering starts and finishes. The effectiveness of the pro-posed method is validated through simulation by comparing it with the conventional IMM algorithm.