• Journal of the Korean Society for Precision Engineering
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• v.21 no.4
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• pp.64-71
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• 2004

The growing need for higher precision and productivity in manufacturing industry has lead to an increased interest in computer numerical control (CNC) systems. It is well known fact that the cross-coupling controller (CCC) is an effective method for contouring applications. In this paper, a multi-axis contour error controller (CEC) based on a contour error vector using parametric curve interpolator is introduced. The contour error vector is a vector from the actual tool position to the nearest point on the desired path. The contour error vector is the closest error model to the contour error. The simulation results show that the CEC is more accurate than the conventional CCC for a biaxial motion system. In addition, the experimental results on 3-axis motion system show that the CEC is simply applied to 3-axis motions and contouring accuracy is significantly improved.

• Journal of Information Technology Services
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• v.2 no.2
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• pp.111-119
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• 2003

In this paper, an efficient block matching algorithm which is based on the Block Sum Pyramid Algorithm (BSPA) is presented. The cost of BSPA[1] was reduced in the proposed algorithm by using l2 norm and partial distortion elimination technique. Motion estimation accuracy of the proposed algorithm is equal to that of BSPA. The efficiency of the proposed algorithm was verified by experimental results.

• The Journal of Korea Robotics Society
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• v.19 no.2
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• pp.203-212
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• 2024

Simultaneous motion is essential in the activities of daily living (ADL). For motion intention recognition, surface electromyogram (sEMG) and corresponding motion label is necessary. However, this process is time-consuming and it may increase the burden of the user. Therefore, we propose a simultaneous motion recognition framework using data augmentation based on muscle activation model. The model consists of multiple point sources to be optimized while the number of point sources and their initial parameters are automatically determined. From the experimental results, it is shown that the framework has generated the data which are similar to the real one. This aspect is quantified with the following two metrics: structural similarity index measure (SSIM) and mean squared error (MSE). Furthermore, with k-nearest neighbor (k-NN) or support vector machine (SVM), the classification accuracy is also enhanced with the proposed framework. From these results, it can be concluded that the generalization property of the training data is enhanced and the classification accuracy is increased accordingly. We expect that this framework reduces the burden of the user from the excessive and time-consuming data acquisition.

• Journal of Broadcast Engineering
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• v.7 no.4
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• pp.310-316
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• 2002

In this paper, we propose an adaptive search range decision algorithm for fast motion estimation of video coding. Block matching algorithm for motion vector estimation that improves coding efficiency by reduction of temporal redundancy has trade-off problem between the motion vector accuracy and the complexity. The proposed algorithm playing as a pre-processing of fast motion estimation adaptively determines the motion search range by the local statistics of neighboring motion vectors. resulting in dramatic reduction of the computational cost without the loss of coding efficiency. Experimental results show the capability of the proposed algorithm.

• Journal of Broadcast Engineering
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• v.23 no.3
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• pp.409-420
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• 2018

In this paper, we propose a Motion Vector-based Particle Filter(MVPF) for object tracking on bitstreams and a object tracking system using the MVPF. The MVPF uses motion vectors to both the transition and the observation models of a general particle filter to improve the accuracy while maintaining the number of particles. In the proposed object tracking system, the state of the target object can be predicted using the histogram of motion vectors extracted from the bitstream. In terms of precision, F-measure and IOU(Intersection Of Union), the proposed method is about 30%, 17%, and 17% better on average, respectively, in MPEG test sequences and VOT2013 sequences. Furthermore, When the tracking results are displayed in box form for subjective performance evaluation, the proposed method can track moving objects more robust than the conventional methods in all test sequences.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.5
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• pp.251-257
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• 2010

In this paper, we propose modified algorithm of motion vector detection for the pixel of image in the optical sensor. It is designed to reduce the amount of operation and have more accuracy in the motion detection than previous block matching algorithm. The proposed algorithm is implemented with Cyclone and fabricated using SEC 0.35um CMOS 1-poly-4-metal technology. The result of test with CARTESIAN ROBOT meets the desired performance.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.4
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• pp.237-247
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• 2003

The Motion Estimation (ME) process is an important part of a video encoding systems since they can significantly reduce bitrate with keeping the output quality of an encoded sequence. Unfortunately this process may dominate the encoding time using straightforward full search algorithm (FS). Up to now, many fast algorithms can reduce the computation complexity by limiting the number of searching locations. This is accomplished at the expense of less accuracy of motion estimation. In this paper, we introduce a new fast motion estimation method based on the spatio-temporal correlation of adjacent blocks. A reliable predicted motion vector (RPMV) is defined. The reliability of RPMV is shown on the basis of motion vectors achieved by FS. The scalar and the direction of RPMV are used in our proposed scheme. The experimental results show that the proposed method Is about l1~14% faster than the nearest neighbor method which is a wellknown conventional fast scheme.

• The Journal of Korea Robotics Society
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• v.3 no.4
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• pp.263-269
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• 2008

Visual odometry is a popular approach to estimating robot motion using a monocular or stereo camera. This paper proposes a novel visual odometry scheme using a stereo camera for robust estimation of a 6 DOF motion in the dynamic environment. The false results of feature matching and the uncertainty of depth information provided by the camera can generate the outliers which deteriorate the estimation. The outliers are removed by analyzing the magnitude histogram of the motion vector of the corresponding features and the RANSAC algorithm. The features extracted from a dynamic object such as a human also makes the motion estimation inaccurate. To eliminate the effect of a dynamic object, several candidates of dynamic objects are generated by clustering the 3D position of features and each candidate is checked based on the standard deviation of features on whether it is a real dynamic object or not. The accuracy and practicality of the proposed scheme are verified by several experiments and comparisons with both IMU and wheel-based odometry. It is shown that the proposed scheme works well when wheel slip occurs or dynamic objects exist.

• Journal of Biomedical Engineering Research
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• v.42 no.4
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• pp.186-192
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• 2021

In this paper, we evaluated prediction accuracy of Euler angle spectrograph classification method using a convolutional neural networks (CNN) for hand gesture recognition in augmented reality (AR) cognitive rehabilitation system based on Leap Motion Controller (LMC). Hand gesture recognition methods using a conventional support vector machine (SVM) show 91.3% accuracy in multiple motions. In this paper, five hand gestures ("Promise", "Bunny", "Close", "Victory", and "Thumb") are selected and measured 100 times for testing the utility of spectral classification techniques. Validation results for the five hand gestures were able to be correctly predicted 100% of the time, indicating superior recognition accuracy than those of conventional SVM methods. The hand motion recognition using CNN meant to be applied more useful to AR cognitive rehabilitation training systems based on LMC than sign language recognition using SVM.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.339-342
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• 2005

We present an efficient block-based motion estimation algorithm with motion analysis. The motion analysis determines a size of search pattern and a maximum repeated count of search pattern. In case of large movement in large image, we reduce search points and the local minimum which caused by low performance. The proposed algorithm employs with searching step of 2. The first step determines an initial search point with neighbor block vector and a size of initial search pattern. The second step determines a size of search pattern and a maximum repeated count with motion analysis. We improve motion prediction accuracy while reducing required computational complexity compared to other fast block-based motion estimation algorithms.