• Transactions of the Korean Society of Mechanical Engineers
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• v.11 no.6
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• pp.923-934
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• 1987

A flexible one-link robotic manipulator is modelled as a rotating cantilever beam with a hub and tip mass. An active control law is developed with consideration of the distributed flexibility of the arm. Equation of motion is derived by Hamilton's principle and, for modal control, represented as state variable form using Galerkin's mode summation method. Feedback coefficients are chosen to minimize the linear quadratic performance index(PI). To reconstruct the complete state vector from the measurements, an observer is proposed. In order to suppress vibration of the manipulator arm to desirable extent and to obtain accuracy of the positioning, weighting factor of input in PI is adjusted. Spillover effect due to the controller which controls several important modes is examined. Experiment is also performed to validate the theoretical analysis.

• Smart Structures and Systems
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• v.4 no.6
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• pp.759-777
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• 2008

A wireless sensing system is designed for application to structural monitoring and damage detection applications. Embedded in the wireless monitoring module is a two-tier prediction model, the auto-regressive (AR) and the autoregressive model with exogenous inputs (ARX), used to obtain damage sensitive features of a structure. To validate the performance of the proposed wireless monitoring and damage detection system, two near full scale single-story RC-frames, with and without brick wall system, are instrumented with the wireless monitoring system for real time damage detection during shaking table tests. White noise and seismic ground motion records are applied to the base of the structure using a shaking table. Pattern classification methods are then adopted to classify the structure as damaged or undamaged using time series coefficients as entities of a damage-sensitive feature vector. The demonstration of the damage detection methodology is shown to be capable of identifying damage using a wireless structural monitoring system. The accuracy and sensitivity of the MEMS-based wireless sensors employed are also verified through comparison to data recorded using a traditional wired monitoring system.

• Journal of radiological science and technology
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• v.42 no.2
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• pp.119-130
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• 2019

Positron emission tomography (PET) is widely used imaging modality for effective and accurate functional testing and medical diagnosis using radioactive isotopes. However, PET has difficulties in acquiring images with high image quality due to constraints such as the amount of radioactive isotopes injected into the patient, the detection time, the characteristics of the detector, and the patient's motion. In order to overcome this problem, we have succeeded to improve the image quality by using the dynamic image reconstruction method based on singular value decomposition. However, there is still some question about the characteristics of the proposed technique. In this study, the characteristics of reconstruction method based on singular value decomposition was estimated over computational simulation. As a result, we confirmed that the singular value decomposition based reconstruction technique distinguishes the images well when the signal - to - noise ratio of the input image is more than 20 decibels and the feature vector angle is more than 60 degrees. In addition, the proposed methode to estimate the characteristics of reconstruction technique can be applied to other spatio-temporal feature based dynamic image reconstruction techniques. The deduced conclusion of this study can be useful guideline to apply medical image into SVD based dynamic image reconstruction technique to improve the accuracy of medical diagnosis.

• Smart Structures and Systems
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• v.32 no.1
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• pp.61-81
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• 2023

This study presents an ensemble learning based Bayesian model updating approach for structural damage diagnosis. In the developed framework, the structure is initially decomposed into a set of substructures. The autoregressive moving average (ARMAX) model is established first for structural damage localization based structural motion equation. The wavelet packet decomposition is utilized to extract the damage-sensitive node energy in different frequency bands for constructing structural surrogate models. Four methods, including Kriging predictor (KRG), radial basis function neural network (RBFNN), support vector regression (SVR), and multivariate adaptive regression splines (MARS), are selected as candidate structural surrogate models. These models are then resampled by bootstrapping and combined to obtain an ensemble model by probabilistic ensemble. Meanwhile, the maximum entropy principal is adopted to search for new design points for sample space updating, yielding a more robust ensemble model. Through the iterations, a framework of surrogate ensemble learning based model updating with high model construction efficiency and accuracy is proposed. The specificities of the method are discussed and investigated in a case study.

• Journal of KIISE:Software and Applications
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• v.29 no.1_2
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• pp.98-113
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• 2002

In this paper, we present advanced algorithms to reduce the computations of block matching algorithms for motion estimation in video coding. Advanced multi-level successive elimination algorithms(AMSEA) are based on the Multi-level successive elimination algorithm(MSEA)[1]. The first algorithm is that when we calculate the sum of absolute difference (SAD) between the sum norms of sub-blocks in MSEA, we use the partial distortion elimination technique. By using the first algorithm, we can reduce the computations of MSEA further. In the second algorithm, we calculate SAD adaptively from large value to small value according to the absolute difference values between pixels of blocks. By using the second algorithm, the partial distortion elimination in SAD calculation can occur early. So, the computations of MSEA can be reduced. In the third algorithm, we can estimate the elimination level of MSEA. Accordingly, the computations of the MSEA related to the level lower than the estimated level can be reduced. The fourth algorithm is a very fast block matching algorithm with nearly 100% motion estimation accuracy. Experimental results show that AMSEA are very efficient algorithms for the estimation of motion vectors.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.8
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• pp.875-881
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• 2014

The redundancy resolution of the seven DOF (Degree of Freedom) upper limb exoskeleton is key to the synchronous motion between a robot and a human user. According to the seven DOF human arm model, positioning and orientating the wrist can be completed by multiple arm configurations that results in the non-unique solution to the inverse kinematics. This paper presents analysis on the kinematic and dynamic aspect of the human arm movement and its effect on the redundancy resolution of the seven DOF human arm model. The redundancy of the arm is expressed mathematically by defining the swivel angle. The final form of swivel angle can be represented as a linear combination of two different swivel angles achieved by optimizing two cost functions based on kinematic and dynamic criteria. The kinematic criterion is to maximize the projection of the longest principal axis of the manipulability ellipsoid of the human arm on the vector connecting the wrist and the virtual target on the head region. The dynamic criterion is to minimize the mechanical work done in the joint space for each of two consecutive points along the task space trajectory. The contribution of each criterion on the redundancy was verified by the post processing of experimental data collected with a motion capture system. Results indicate that the bimodal redundancy resolution approach improved the accuracy of the predicted swivel angle. Statistical testing of the dynamic constraint contribution shows that under moderate speeds and no load, the dynamic component of the human arm is not dominant, and it is enough to resolve the redundancy without dynamic constraint for the realtime application.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.2
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• pp.179-185
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• 2015

Although an accelerometer is a sensor that measures acceleration, it cannot be used by itself to measure the acceleration when the orientation of the sensor changes. This paper introduces a Kalman filter for the estimation of a sensor acceleration based on a six-axis inertial sensor (i.e., a three-axis accelerometer and three-axis gyroscope). The novelty of the proposed Kalman filter lies in the fact that its state vector includes not only the tilt angle variable but also the sensor acceleration. Thus, the filter can explicitly estimate the latter with a high accuracy. The accuracy of acceleration estimates were validated experimentally under three different dynamic conditions, using an optical motion capture system. It could be concluded that the performance of the proposed Kalman filter was comparable to that of the state-of-the-art estimation algorithm employed by the Xsens MTw. The proposed algorithm may be more suitable than inertial/magnetic sensor-based algorithms for various applications adopting six-axis inertial sensors.

• Journal of information and communication convergence engineering
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• v.17 no.1
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• pp.74-83
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• 2019

This study designs a squat posture recognition system that can provide correct squat posture guidelines. This system comprises two modules: a Kinect camera for monitoring users' body movements and a Wii Balance Board(WBB) for measuring balanced postures with legs. Squat posture recognition involves two states: "Stand" and "Squat." Further, each state is divided into two postures: correct and incorrect. The incorrect postures of the Stand and Squat states were classified into three and two different types of postures, respectively. The factors that determine whether a posture is incorrect or correct include the difference between shoulder width and ankle width, knee angle, and coordinate of center of pressure(CoP). An expert and 10 participants participated in experiments, and the three factors used to determine the posture were measured using both Kinect and WBB. The acquired data from each device show that the expert's posture is more stable than that of the subjects. This data was classified using a support vector machine (SVM) and $na{\ddot{i}}ve$ Bayes classifier. The classification results showed that the accuracy achieved using the SVM and $na{\ddot{i}}ve$ Bayes classifier was 95.61% and 81.82%, respectively. Therefore, the developed system that used Kinect and WBB could classify correct and incorrect postures with high accuracy. Unlike in other studies, we obtained the spatial coordinates using Kinect and measured the length of the body. The balance of the body was measured using CoP coordinates obtained from the WBB, and meaningful results were obtained from the measured values. Finally, the developed system can help people analyze the squat posture easily and conveniently anywhere and can help present correct squat posture guidelines. By using this system, users can easily analyze the squat posture in daily life and suggest safe and accurate postures.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.4
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• pp.41-49
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• 2008

H.264/AVC supports variable block motion compensation, multiple reference images, 1/4-pixel motion vector accuracy, and in-loop deblocking filter, compared with the existing compression technologies. While these coding technologies are major functions of compression rate improvement, they lead to high complexity at the same time. For the H.264 video coding technology to be actually applied on low-end / low-bit rates terminals more extensively, it is essential to improve tile coding speed. Currently the deblocking filter that can improve the moving picture's subjective image quality to a certain degree is used on low-end terminals to a limited extent due to computational complexity. In this paper, a performance improvement method of the deblocking filter that efficiently reduces the blocking artifacts occurred during the compression of low-bit rates digital motion pictures is suggested. In the method proposed in this paper, the image's spatial correlational characteristics are extracted by using the variable block information of motion compensation; the filtering is divided into 4 modes according to the characteristics, and adaptive filtering is executed in the divided regions. The proposed deblocking method reduces the blocking artifacts, prevents excessive blurring effects, and improves the performance about $30{\sim}40%$ compared with the existing method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.2
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• pp.79-88
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• 2004

Gaze detection is to locate the position on a monitor screen where a user is looking by computer vision. Gaze detection systems have numerous fields of application. They are applicable to the man-machine interface for helping the handicapped to use computers and the view control in three dimensional simulation programs. In our work, we implement it with a computer vision system setting a IR-LED based single camera. To detect the gaze position, we locate facial features, which is effectively performed with IR-LED based camera and SVM(Support Vector Machine). When a user gazes at a position of monitor, we can compute the 3D positions of those features based on 3D rotation and translation estimation and affine transform. Finally, the gaze position by the facial movements is computed from the normal vector of the plane determined by those computed 3D positions of features. In addition, we use a trained neural network to detect the gaze position by eye's movement. As experimental results, we can obtain the facial and eye gaze position on a monitor and the gaze position accuracy between the computed positions and the real ones is about 4.8 cm of RMS error.