• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.161-167
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• 1993

This paper presents robustness properties of the Kalman Filter ad the associated LQG/LTR method for linear time-invariant systems having delays in both the state and output. A circle condition relating to the return difference matrix associated with the Kalman filter is derived. Using this circle condition, it is shown that the Kalman filter guarantees(1/2, .inf.) gain margin and .+-.60.deg. phase margin, which are the same as those for nondelay systems. However, it is shown that, even for minimum phase plants, the LQG/LTR method can not recover the target loop transfer function. Instead, an upper bound on the recovery error is obtained using an upper bound of the solution of the Kalman filter Riccati equations. Finally, some dual properties between output-delated system and input-delayed systems are exploited.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.2
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• pp.197-208
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• 1996

A neural network-based PSRI(position, scale, and rotation invariant) feature extraction and ATR (automatic target recognition) system are proposed and an efficient parallel implementatio of the proposed system using multicomputer is also presented. In the proposed system, the scale and rotationinvariant features are extracted from the contour projection of the number of edge pixels on each of the concentric circles, which is input t the cooperative network. We proposed how to decide the optimum depth and the width of the parallel pipeline system for real time applications by modeling the proposed system into a parallel pipeline implementation method using transputers is also proposed. The implementation results show that we can extract PSRI features less sensitive to input variations, and the speedup of the proposed ATR system is about 7.55 for the various rotated and scaled targets using 8-node transputer system.

• Journal of Biomedical Engineering Research
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• v.26 no.3
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• pp.133-138
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• 2005

This paper presents a new method for measuring ocular torsion using the optical flow. Images of the iris were cropped and transformed into rectangular images that were orientation invariant. Feature points of the iris region were selected from a reference and a target image, and the shift of each feature was calculated using the iterative Lucas-Kanade method. The feature points were selected according to the strength of the corners on the iris image. The accuracy of the algorithm was tested using printed eye images. In these images, torsion was measured with $0.15^{\circ}$ precision. The proposed method shows robustness even with the gaze directional changes and pupillary reflex environment of real-time processing.

• Journal of Communications and Networks
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• v.9 no.4
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• pp.438-445
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• 2007

In this paper, we consider the problem of optimum selection of quality-of-service(QoS) classes in interworking between the networks in a next-generation-network(NGN) environment. After introducing the delay-cost and loss-cost characteristics, we discuss the time-invariant(TI) and time-variant(TV) scenarios. For the TI case, we show that under nearly lossless transmission condition, each network can make its own optimization regardless of other networks. For the TV case, we present sufficient conditions under which the optimum QoS class of each network can be considered fixed with respect to time without considerable degradation in the optimization target. Therefore, under the conditions presented in this paper, the QoS of a flow in each network can be determined solely by considering the characteristics of that network and this QoS class can be held fixed during the flow period.

• Journal of Information Processing Systems
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• v.17 no.1
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• pp.124-135
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• 2021

Object detection is a fundamental yet challenging task in computer vision that plays an important role in object recognition, tracking, scene analysis and understanding. This paper aims to propose a multiproperty fusion algorithm for moving object detection. First, we build a scale-invariant feature transform (SIFT) vector field and analyze vectors in the SIFT vector field to divide vectors in the SIFT vector field into different classes. Second, the distance of each class is calculated by dispersion analysis. Next, the target and contour can be extracted, and then we segment the different images, reversal process and carry on morphological processing, the moving objects can be detected. The experimental results have good stability, accuracy and efficiency.

• The KIPS Transactions:PartB
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• v.17B no.5
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• pp.355-362
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• 2010

Face tracking is to estimate the motion of a non-rigid face together with a rigid head in 3D, and plays important roles in higher levels such as face/facial expression/emotion recognition. In this paper, we propose an AAM-based face tracking algorithm. AAM has been widely used to segment and track deformable objects, but there are still many difficulties. Particularly, it often tends to diverge or converge into local minima when a target object is self-occluded, partially or completely occluded. To address this problem, we utilize the scale invariant feature transform (SIFT). SIFT is an effective method for self and partial occlusion because it is able to find correspondence between feature points under partial loss. And it enables an AAM to continue to track without re-initialization in complete occlusions thanks to the good performance of global matching. We also register and use the SIFT features extracted from multi-view face images during tracking to effectively track a face across large pose changes. Our proposed algorithm is validated by comparing other algorithms under the above 3 kinds of occlusions.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.18 no.10
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• pp.1157-1162
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• 2007

In this paper, E-TDLNN(Expanded-Tapped Delay Line Neural Network) method is suggested to make the model of power amplifier effectively. This method is the one for making the model of power amplifier through the study in neural network to the target value, the measured output spectrum of power amplifier, after adding the external value factor, gate bias, as an invariant input to the TDLNN method which suggested the memory effect of power amplifier effectively. To prove the validity of suggested method, the data at 2 points, 3.45 V and 3.50 V of gate bias range $3.4{\sim}3.6V$ with the 0.01 V step change, are studied and the predicted results at the gate bias 3.40 V, 3.48 V, 3.53 V and 3.60 V shows good coincidence with the measured values.

• Journal of Life Science
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• v.28 no.6
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• pp.753-763
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• 2018

Cysteine cathepsins are lysosomal enzymes that belong to the papain family and can induce the degradation of damaged proteins through the endo-lysosomal pathway. It is highly upregulated in many cancers by regulating gene amplification and transcriptional, translational, and post-transcriptional modifications. Cathepsin S is part of the cysteine cathepsin family. Many studies have demonstrated that cathepsin S not only plays a specific role in MHC class II antigen presentation but also plays a crucial role in cancers. Cathepsin S is more stable at a neutral pH compared to other cysteine cathepsins, which supports the importance of cathepsin S in disease microenvironments. Therefore, the dysregulation of cathepsin S has participated in a variety of pathological processes, including cancer, arthritis, and cardiovascular disease. Furthermore, a decrease or depletion in the expression of cathepsin S has been implicated in the processes of tumor growth, invasion, metastasis, and angiogenesis. Taken together, cathepsin S has been suggested as an attractive therapeutic target for cancer therapy. In this review, the known involvement of cathepsin S in diseases, particularly with respect to recent work indicating its role in cancer therapy, is examined. An overview of current literature on the inhibitors of cathepsin S as a therapeutic target for cancer is also provided.

• Journal of KIISE:Software and Applications
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• v.34 no.10
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• pp.937-944
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• 2007

In this paper, we propose multiple human tracking with an moving stereo camera. The tracking process is based on mean shift algorithm which is using color information of the target. Color based tracking approach is invariant to translation and rotation of the target but, it has several problems. Because of mean shift uses color distribution, it is sensitive to color distribution of background and targets. In order to solve this problem, we combine color and depth information of target. Also, we build human body part model to handle occlusions and we have created adaptive box scale. As a result, the proposed method is simple and efficient to track multiple humans in real time.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.7
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• pp.774-781
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• 2014

The radar measurements are composed of range, Doppler and angles which are expressed as polar-coordinate components. An approach to match the measurements with the states of target dynamics which are modeled in cartesian coordinates is to use the pseudo-measurements or the extended Kalman filter in order to solve the mismatching problem. Another approach is that the states of dynamics are modeled in polar coordinates and measurement equation is linear. However, this approach bears that we have to deal with a time-varying dynamics. In this study, it is proposed that the states of dynamics are expressed as polar-coordinate component and the system matrix of the dynamic equation is modeled as a time-invariant. Nonlinear terms that appear due to the proposed modeling are regarded as a system input. The results of a series of simulation runs indicate that the tracking filter that uses the proposed modeling is viable from the fact that the Doppler measurement is easy to be augmented in the measurement equation.