• Journal of Biomedical Engineering Research
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• v.8 no.2
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• pp.171-176
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• 1987

Wiener filtering method was applied to the dual energy imaging procedure in digital radiography(D.R.). A linear scanning photodiode arrays with 1024 elements(0.6mm H 1.3mm pixel size) were used to obtain chest images in 0.7 sec. For high energy image acquisition, X-ray tube was set at 140KVp, 100mA with a rare-earth phosphor screen. Low energy image was obtained with X-ray tube setting at 70KVp, 150mA. These measured dual energy images are represented in the vector matrix notation as a linear discrete model including the additive random noise. Then, the object images are restored in the minimum mean square error sense using Wiener filtering method in the transformed domain. These restored high and low energy images are used for computation of the basis image decomposition. Then the basis images are linearly combined to produce bone or tissue selective images. Using this process, we could improve the signal to noise ratio characteristics in the material selective images.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.241-245
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• 2005

Strapdown inertial navigation system (SINS) integrated with astronavigation system (ANS) yields reliable mission capability and enhanced navigational accuracy for spacecrafts. The theory and characteristics of integrated system based on unscented Kalman filtering is investigated in this paper. This Kalman filter structure uses unscented transform to approximate the result of applying a specified nonlinear transformation to a given mean and covariance estimate. The filter implementation subsumed here is in a direct feedback mode. Axes misalignment angles of the SINS are observation to the filter. A simple approach for simulation of axes misalignment using stars observation is presented. The SINS error model required for the filtering algorithm is derived in space-stabilized mechanization. Simulation results of the integrated navigation system using a medium accuracy SINS demonstrates the validity of this method on improving the navigation system accuracy with the estimation and compensation for gyros drift, and the position and velocity errors that occur due to the axes misalignments.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.129-132
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• 1999

This paper deals with the optimal filtering problem constrained to input noise signal corrupting the measurement output for linear discrete-time systems. The transfer matrix H$_2$and/or H$_{\infty}$ norms are used as criteria in an estimation error sense. In this paper, the mixed $H_2/H_{\infty}$ filtering Problem in lineal discrete-time systems is solved using the LMI approach, yielding a compromise between the H$_2$and H$_{\infty}$ filter designs. This filter design problems we formulated in a convex optimization framework using linear matrix inequalities. A numerical example is presented.

• Journal of the Korean Data and Information Science Society
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• v.18 no.3
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• pp.659-668
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• 2007

Collaborative filtering approach for recommender systems are now widely applied in e-commerce to assist customers to find their needs from many that are frequently available. this approach makes recommendations for users based on the opinions to similar users in the system. But this approach is opened to users who present their preference to items or acquire the preference information form other users, noise in the system makes significant problem for accurate recommendation. In this paper, we analyze the relationship between the standard deviation of preference ratings for each user and the estimated ratings of them. The result shows that the possibility of the pre-filtering condition which detecting the factor of bad effect on the prediction of user's preference. It is expected that using this result will reduce the possibility of bad effect on recommender systems.

• Journal of the Korean Data and Information Science Society
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• v.20 no.3
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• pp.505-514
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• 2009

The researcher analyzes the relationship between the number of neighbors and the prediction accuracy in the preference prediction process using collaborative filtering system. The number of neighbors who are involved in the preference prediction process are divided into four groups. Each group shows a little difference in the preference prediction. By using prediction error averages in each group, linear functions are suggested. Through the result of this study, the accuracy of preference prediction can be raised when using linear functions by using the number of neighbors in the suggested system.

• Journal of Sensor Science and Technology
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• v.25 no.6
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• pp.418-422
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• 2016

A filtering algorithm based on the decentralized moving average Kalman filter with uncertainties is proposed in this paper. The proposed filtering algorithm presented combines the Kalman filter with the moving average strategy. A decentralized fusion algorithm with the weighted sum structure is applied to the local moving average Kalman filters (LMAKFs) of different window lengths. The proposed algorithm has a parallel structure and allows parallel processing of observations. Hence, it is more reliable than the centralized algorithm when some sensors become faulty. Moreover, the choice of the moving average strategy makes the proposed algorithm robust against linear discrete-time dynamic model uncertainties. The derivation of the error cross-covariances between the LMAKFs is the key idea of studied. The application of the proposed decentralized fusion filter to dynamic systems within a multisensor environment demonstrates its high accuracy and computational efficiency.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.888-890
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• 2003

The objective of this investigation is to establish a simple yet effective block adjustment procedure for the orthorectification of multi-orbit satellite images. The major works of the proposed scheme are: (1) adjustment of satellite‘s orbit accurately, (2) calculation of the error vectors for each tie point using digital terrain model and ray tracing technique, (3) refining the orbit using the Least Squares Filtering technique and (4) generation of the orthophotos. In the process of least squares filtering, we use the residual vectors on ground control points and tie points to collocate the orbit. In orthorectification, we use the indirect method to generate the orthoimage. Test areas cover northern Taiwan. Test images are from SPOT 5 satellite. Experimental results indicate that proposed method improves the relative accuracy significantly.

• Investigative Magnetic Resonance Imaging
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• v.4 no.2
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• pp.120-127
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• 2000

Adaptive template filtering has been proposed recently for enhancement of signal-to-noise ratio without loss of resolution. In the adaptive template filtering, an optimal template among multiple templates is selected, then linear least square error filtering based on the template is applied in vowel by vowel basis. In some magnetic resonance imaging, where the distribution of gray level has relatively small dynamic range, e.g., $T_1$ imaging, however, artificial stair-like artifact is observed at near edges. This is partially due to the edge enhancement effect in such yokels that contain multiple compounds at the boundaries of tissues. The gray levels of these yokels become similar gray levels of near dominant vowels that contain single compound by the adaptive filtering, which enlarges edge discontinuities. In this paper, we propose a technique to eliminate such artifact by identifying those yokels that contain multiple compounds and assigning the largest template for them. Filtered images with the proposed technique show substantial visual enhancement at the edges without degradation of peak signal-to-noise ratio compared to the original adaptive template filtering for both magnetic resonance images and phantom images.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.4
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• pp.195-201
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• 2015

For median filtering (MF) detection in altered digital images, this paper presents a new feature vector that is formed from autoregressive (AR) coefficients via an AR model of the gradients between the neighboring row and column lines in an image. Subsequently, the defined 10-D feature vector is trained in a support vector machine (SVM) for MF detection among forged images. The MF classification is compared to the median filter residual (MFR) scheme that had the same 10-D feature vector. In the experiment, three kinds of test items are area under receiver operating characteristic (ROC) curve (AUC), classification ratio, and minimal average decision error. The performance is excellent for unaltered (ORI) or once-altered images, such as $3{\times}3$ average filtering (AVE3), QF=90 JPEG (JPG90), 90% down, and 110% up to scale (DN0.9 and Up1.1) images, versus $3{\times}3$ and $5{\times}5$ median filtering (MF3 and MF5, respectively) and MF3 and MF5 composite images (MF35). When the forged image was post-altered with AVE3, DN0.9, UP1.1 and JPG70 after MF3, MF5 and MF35, the performance of the proposed scheme is lower than the MFR scheme. In particular, the feature vector in this paper has a superior classification ratio compared to AVE3. However, in the measured performances with unaltered, once-altered and post-altered images versus MF3, MF5 and MF35, the resultant AUC by 'sensitivity' (TP: true positive rate) and '1-specificity' (FN: false negative rate) is achieved closer to 1. Thus, it is confirmed that the grade evaluation of the proposed scheme can be rated as 'Excellent (A)'.

• Journal of the Korea Safety Management & Science
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• v.14 no.1
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• pp.217-224
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• 2012

Location-Based Services(LBS) is a service that provide location information by using communication network or satellite signal. In order to provide LBS precisely and efficiently, we studied how we can reduce the error on location determination of objects such people and things. We focus on using the least square method and triangulation positioning method to improves the accuracy of the existing location determination method. Above two methods is useful if the distance between the AP and the tags can be find. Though there are a variety of ways to find the distance between the AP and tags, least squares and triangulation positioning method are wildely used. In this thesis, positioning method is composed of preprocessing and calculation of location coordinate and detail of methodology in each stage is explained. The distance between tag and AP is adjusted in the preprocessing stage then we utilize least square method and triangulation positioning method to calculate tag coordinate. In order to confirm the performance of suggested method, we developed the test program for location determination with Labview2010. According to test result, triangulation positioning method showed up loss error than least square method by 38% and also error reduction was obtained through adjustment process and filtering process. It is necessary to study how to reduce error by using additional filtering method and sensor addition in the future and also how to improve the accuracy of location determination at the boundary location between indoor and outdoor and mobile tag.